{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Titanic.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wda10BWxHDaz",
        "colab_type": "code",
        "outputId": "e2ceb930-40dd-44d0-f8b7-f16bd5e55708",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "from google.colab import files\n",
        "import os\n",
        "files.upload()\n",
        "os.listdir()\n",
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "import kaggle\n",
        "print(\"Imported kaggle api successfully !\")\n",
        "!kaggle competitions download -c titanic\n",
        "print(\"Downloaded Titanic dataset successfully!\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-47147a0f-af8e-4573-b712-657c82ed3b97\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-47147a0f-af8e-4573-b712-657c82ed3b97\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "Imported kaggle api successfully !\n",
            "Downloading train.csv to /content\n",
            "  0% 0.00/59.8k [00:00<?, ?B/s]\n",
            "100% 59.8k/59.8k [00:00<00:00, 58.0MB/s]\n",
            "Downloading test.csv to /content\n",
            "  0% 0.00/28.0k [00:00<?, ?B/s]\n",
            "100% 28.0k/28.0k [00:00<00:00, 23.8MB/s]\n",
            "Downloading gender_submission.csv to /content\n",
            "  0% 0.00/3.18k [00:00<?, ?B/s]\n",
            "100% 3.18k/3.18k [00:00<00:00, 3.19MB/s]\n",
            "Downloaded Titanic dataset successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_Ms3nRUIOTJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgftjPVDlqe5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import (RandomForestClassifier,\n",
        "                             AdaBoostClassifier,\n",
        "                             GradientBoostingClassifier)\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import (GridSearchCV,\n",
        "                                     cross_val_score,\n",
        "                                     StratifiedKFold, \n",
        "                                     learning_curve)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DURZyLZboDUq",
        "colab_type": "text"
      },
      "source": [
        "## Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpiBQbXeJZEX",
        "colab_type": "code",
        "outputId": "00608e99-5a89-46c5-90c7-57e7ba483989",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "train = pd.read_csv('train.csv')\n",
        "test = pd.read_csv('test.csv')\n",
        "ids = test['PassengerId']\n",
        "print('Train shape : ',train.shape)\n",
        "print('Test shape : ',test.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape :  (891, 12)\n",
            "Test shape :  (418, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghDZMrqQJwvU",
        "colab_type": "code",
        "outputId": "b62f96c6-b51b-4d32-e125-3ec4e0c7a74d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rvjcTY9Jy3z",
        "colab_type": "code",
        "outputId": "940c2163-28e0-478a-981c-f3f3b56bf638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass                                          Name     Sex  \\\n",
              "0          892       3                              Kelly, Mr. James    male   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
              "3          895       3                              Wirz, Mr. Albert    male   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
              "\n",
              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
              "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
              "1  47.0      1      0   363272   7.0000   NaN        S  \n",
              "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
              "3  27.0      0      0   315154   8.6625   NaN        S  \n",
              "4  22.0      1      1  3101298  12.2875   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snbyn0bsJ06g",
        "colab_type": "text"
      },
      "source": [
        "## Cleaning and preprocessing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vbVbAavKCP1",
        "colab_type": "code",
        "outputId": "70be7353-82b1-4a4e-de29-cb16abc304b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "train.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin          687\n",
              "Age            177\n",
              "Embarked         2\n",
              "Fare             0\n",
              "Ticket           0\n",
              "Parch            0\n",
              "SibSp            0\n",
              "Sex              0\n",
              "Name             0\n",
              "Pclass           0\n",
              "Survived         0\n",
              "PassengerId      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-WEmE--KKdS",
        "colab_type": "code",
        "outputId": "ba4f24da-35c6-4216-fbe9-5f650c0c04f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "train['Embarked'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Skctc4fsKxF9",
        "colab_type": "code",
        "outputId": "1213212a-a453-4cbd-95e7-f8dc9bde06b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "train['Embarked'].fillna('S',inplace=True)\n",
        "train.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin          687\n",
              "Age            177\n",
              "Embarked         0\n",
              "Fare             0\n",
              "Ticket           0\n",
              "Parch            0\n",
              "SibSp            0\n",
              "Sex              0\n",
              "Name             0\n",
              "Pclass           0\n",
              "Survived         0\n",
              "PassengerId      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKazTHAvLAOf",
        "colab_type": "code",
        "outputId": "9019fb16-e7f0-455b-9b61-40f3b5de3153",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.scatterplot(x='Age',y='SibSp',data=train)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130d507518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/u22/l1qx60S72Knk/HLoNzbIvAvNqxOx/URZ8H8C4B0icpmIZADcDOC7nZxg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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmlzDzjJLZlh",
        "colab_type": "code",
        "outputId": "d9900b68-540a-4f93-a38c-9a12d5cb0e1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.scatterplot(x='Age',y='Parch',data=train)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130d460cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P3e2E3ZoO48gPC9G2nygT/o8BvFNELheRFIDbAPwwwv7mpb87hT3bs7WJd6+j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\nCZ+IyBD/H0yf4tRfnD54AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6qGxdDkLfRt",
        "colab_type": "code",
        "outputId": "d4b3422e-0652-4358-c429-9b118b44104b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "train[['Age','SibSp']].groupby('SibSp').median()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Age\n",
              "SibSp      \n",
              "0      29.0\n",
              "1      30.0\n",
              "2      23.0\n",
              "3       9.5\n",
              "4       6.5\n",
              "5      11.0\n",
              "8       NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWwzJUsnNZa5",
        "colab_type": "code",
        "outputId": "4683c7d0-117b-4b64-9f7a-5527fee78b24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "train[['Age','SibSp']].groupby('SibSp').mean()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31.397558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30.089727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.055556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Age\n",
              "SibSp           \n",
              "0      31.397558\n",
              "1      30.089727\n",
              "2      22.620000\n",
              "3      13.916667\n",
              "4       7.055556\n",
              "5      10.200000\n",
              "8            NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ELyoSdqMYsA",
        "colab_type": "code",
        "outputId": "a737c74e-16a4-4fde-afed-efff6bf4f31e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "train[['Age','Parch']].groupby('Parch').median()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Age\n",
              "Parch      \n",
              "0      30.0\n",
              "1      23.0\n",
              "2      16.5\n",
              "3      24.0\n",
              "4      42.5\n",
              "5      39.0\n",
              "6      43.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ay6Kd174NfV7",
        "colab_type": "code",
        "outputId": "08766fea-b4c7-4293-b587-18cc9234d351",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "train[['Age','Parch']].groupby('Parch').mean()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>32.178503</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>24.422000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.216912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>44.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>39.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>43.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Age\n",
              "Parch           \n",
              "0      32.178503\n",
              "1      24.422000\n",
              "2      17.216912\n",
              "3      33.200000\n",
              "4      44.500000\n",
              "5      39.200000\n",
              "6      43.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCSfNeSjNf8y",
        "colab_type": "code",
        "outputId": "46021e6c-be00-44c3-939b-100adca6a3c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "train[train['SibSp']==8]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Master. Thomas Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>181</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Miss. Constance Gladys</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>202</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Mr. George John Jr</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>793</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Miss. Stella Anna</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>847</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Mr. Douglas Bullen</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>863</th>\n",
              "      <td>864</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
              "159          160         0       3         Sage, Master. Thomas Henry    male   \n",
              "180          181         0       3       Sage, Miss. Constance Gladys  female   \n",
              "201          202         0       3                Sage, Mr. Frederick    male   \n",
              "324          325         0       3           Sage, Mr. George John Jr    male   \n",
              "792          793         0       3            Sage, Miss. Stella Anna  female   \n",
              "846          847         0       3           Sage, Mr. Douglas Bullen    male   \n",
              "863          864         0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n",
              "\n",
              "     Age  SibSp  Parch    Ticket   Fare Cabin Embarked  \n",
              "159  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "180  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "201  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "324  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "792  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "846  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "863  NaN      8      2  CA. 2343  69.55   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKQSTxYeN_ZS",
        "colab_type": "text"
      },
      "source": [
        "We see that all the members are either Miss, Master or Mr. which suggests that there was no children or son or daughter. We can fill the mean or median values to age."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bM346OODOaAL",
        "colab_type": "code",
        "outputId": "952e32b8-76bc-4abb-85e8-a2965def9e1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Mean of age is : ',train['Age'].mean())\n",
        "print('Median of age is : ',train['Age'].median())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of age is :  29.69911764705882\n",
            "Median of age is :  28.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCGgJcR-Ou3y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['Age'].fillna(train['Age'].median(),inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rf8ppJEFO6dU",
        "colab_type": "code",
        "outputId": "77ca1db4-d610-4505-a2ae-0a3c5aabec6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "train.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin          687\n",
              "Embarked         0\n",
              "Fare             0\n",
              "Ticket           0\n",
              "Parch            0\n",
              "SibSp            0\n",
              "Age              0\n",
              "Sex              0\n",
              "Name             0\n",
              "Pclass           0\n",
              "Survived         0\n",
              "PassengerId      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4MpkcuxPFC5",
        "colab_type": "code",
        "outputId": "a1e1344a-4661-4f10-9930-5e3ee0103bc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "## lets check the heatmap before decided about the cabin column\n",
        "sns.heatmap(train.corr(),annot=True)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130b390080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VvEGZ8ikR1UVWPFLKbClliJTyA2A00B/IypODZYFFUvIsewOIFUK0RF+JrC9i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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tg10TDc7PqFS",
        "colab_type": "text"
      },
      "source": [
        "From the above the map, we  can find the following relations :\n",
        "\n",
        "1. Fare and survived\n",
        "\n",
        "2. Fare and SibSp\n",
        "\n",
        "3. Fare and Parch\n",
        "\n",
        "4. Parch and SIbSp\n",
        "\n",
        "5. Age and fare (less correlated but still positive)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WaMBr214QbXm",
        "colab_type": "code",
        "outputId": "db7ef0db-49f2-45c9-a31c-99b78241f89b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "## lets add family size and check the correlation then\n",
        "train['FamilySize'] = train['Parch'] + train['SibSp'] +1\n",
        "train.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>FamilySize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  FamilySize  \n",
              "0      0         A/5 21171   7.2500   NaN        S           2  \n",
              "1      0          PC 17599  71.2833   C85        C           2  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S           1  \n",
              "3      0            113803  53.1000  C123        S           2  \n",
              "4      0            373450   8.0500   NaN        S           1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9T2oDBKNYJTw",
        "colab_type": "code",
        "outputId": "1d323d15-0797-474b-8980-530d22a5356e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "sns.heatmap(train.corr(),annot=True)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130b029668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T7m0SImJZ3HNS6TgXKNYgv+m9yrgtenQCeXgP6CCEOA10AO5guP/+bf7uwHcFIE5KmSyE\neBJDtxXGWVjpUsr1QojLwGohhAaoLqXcJ4Q4BPQHHICdwGghxGgppRRCNJVSnn5ImseBr4QQzkAi\nhq6wB911u4DRwOdGHT5SypBCY8nlAPASMF0I0R1wfoT9Y/H+h7M4fvos8fEJdO77MiOHDaJfr64l\nEvf1vSF4dmzCfw7OISMlnR3v5darwdtnsLL7JAB2T15O9znDjVNTz3Bjn2F20h8LNtPr29E0erED\nCXfusfmNbwB4okdLmgzqTHZmFpmpGWwZNb9IDaF7Q/Du6MObB74g0zj18wGvbZvJ4h4TAdg++Xt6\nG6fHXgs+Q6hRQ6fx/XH1ckdmS+7fuce2icsAqNejJb4vdyE7M4uM1Aw2jJ5XpAaZlc2hKSvouXoc\nQqvh8tr9xF25g+/Yftw9e4O/gk5x6af9dPpqBAMOziEtXk/Qm4b4dC3q0nRkL7Izs5DZkoOTlpv9\nJJ2fK/tCqNPRh7f3f2GYpvx+bl68sW0m3xrzYsuU73lm9utY2lhxNfhMzmyxwAkDcK9fEykl8WF3\n2WTMi2Mrg+j7+euM2vUpCMHpdfuJunQ7T9o39obg1bEJw4x1YadJXRi0fQarjHVhz+TldCukLhxb\nsJmnvx1NQ2Nd2GKsC3ZuFXh5yzSsHGyR2dk0G9aN5Z3/S7o+BQtba2q2b0jQhGUFyiN4ygr6rjKU\nx8W1+4m9cge/d/sRde4GN4JOcWHtfrp+NYIhB+aQGq9n+6jc8n318JdYOdqisbTAq6svG1+eRWqc\nnt5L30VrZQEaQdjvf3Ju9Z5ilc/j1tV/EilL7EXLOxh6mh5QzRhmkpYMx9irY+wB6ielzOt5i4l4\n1GwdIYReSumQL8waQ1dXLQzdYhWBqUAchnGXBy2jCcBuDP15FTC0WlZLKWcJIWyBr4A2RvsbUsqn\nhRCvAL5SylHGtLYAs6WUwcYm4PtALHAJCJNSTjI6tvlAPQxO84CUcoQQYiqgl1LONsblD7xnTMcV\nWIOh6fg7hi675lLKIucdqg+O5aI+OJaL+uBYLuXhxbr/pQ+OpYZsMftkbHyeLjI9Y6/RFaAzBsdy\nHHhJSnnBxKYSECulzBZCzACypJSPdcN5pIMpTwghHKSUemNm/Qosk1IW/WJECaMcTC7KweSiHEwu\nysHkUiIO5tQm8x1Ms94PTU8I0QPDQ70Ww71zhhDiY+CElHKTEOI5DDPHJIYenjellGlFx/hoykN9\nKA5ThRBdMEwI2EXuhAGFQqH436MEl4qRUm4DtuUL+8Dk9y9AiU6P+1c5GCnle4+2UigUiv8Rsgq+\naPpv4l/lYBQKheL/FeV0CRhzUQ5GoVAoyitqNWWFQqFQlAqqBaNQKBSKUkE5GIVCoVCUBlIN8isU\nCoWiVFBjMP9/KA8vOb516uOylgBA60ZDyloC3ayrP9roH8Balv1Ljrpy8uHDxtaltCZXMQjOrPBo\no38LqotMoVAoFKWCasEoFAqFolRQLRiFQqFQlAqqBaNQKBSKUiGznAyu/U2Ug1EoFIryimrBKBQK\nhaJUUGMwCoVCoSgVVAtGoVAoFKWCasH8/6bTR4Pw7OhDZkoa28cuIvr8zQI2VRrVopvx29439oWw\n98NVANhUsOfpBaOoUM2N+2F32TzyG9LuJ1Pdrx59l7zD/dt3Abi64zhHvn78b6tNnvkFBw4fw8W5\nIhtXL3zs+B7Fe9Peom1nP1JT0pj69kwun7tSwGbuj7OpVNkVrYWWkD/O8OmEL8k2uagGvv4i70wd\nRecGT3M/1ryX+Hp+OJi6HX3ISEln/XsLibhws4CNR0NPnp39OpY2VlzZF8LWj1bm7PMbEkirwYFk\nZ2VzZe9pds5aQ8VqlXhr92zuXQ8H4PbpUDZNWlYg3gd0nToY745NyDB+8z2ykHqha1iLPnNGYGFj\nSei+M+ycatDgP/Y56gY0R2ZLkmIS2DR2IfroeGr61eOFxe8Sb6wXl3Yc5+Dcoj/o6u7fGN9pgxAa\nDaFrgrk4b3Oe/RorC9rMHYFLI0/S4hI5NGIeSWH3EBZa/Gb/B5dGtRAWGm6sO8SFeZux83Ch9dcj\nsHWrgJSS0NX7uLx0Z5Hp58fhqWZ4fPgaaDTErQ3i7sK837aqNKwPzi8GIrOyyIpJIOy/X5Nxx3Cu\nlh5uVJ01Gkv3SiAlN1/9iIw70WanXcO/MU9NHYTQari4JpiTCwrmReBXI3Br5ElqXCI7Rs4jMewe\nNhUd6P7dGCo38eLSugPsn2IoI0t7G/qtn5J7bu4uXN5wmIMfrTZbk1moFox5CCEmAS8BWUA28LqU\n8o/HjLM3UF9KOasE9OmllA7FOcazYxOca+lY+tRY3JvWJmDGK/zQZ2oBuy4zXmXXf5cQcfoa/Va8\nj6d/Y24En6Xlm724dfgixxZspuXIXrQa2YsDn6wFIOz4ZX59dc7jnlYe+vYI4KV+vZk4bXaJxlsY\nbTv5Ud2rGs+0GUDDZvWZMGssr/R8vYDdhOEfkKRPBuCzJdPo0qsju37bA0AVj8r4+bckIizS7HTr\n+vvg6qnjS/93qdbUm94zhvJd34IrMPSePpSNE5YQdjqUwcvHUce/CVeDz+DZuj71AnyZ1308WemZ\n2Ls65RwT+1cU83tMfKQG745NcPHUMb/DWKo29abH9FdZ1vfDAnY9Zgxly/gl3DkdyoAV46jt34Rr\nwWf4/butBM8x3HxbvNKVp956lm1GZ3br+GXWDn10+QmNoMXMIeztP4vkiFi6bfuYsJ0nSbganmNT\ne4A/6fFJbGo7lpp9/Gg6uT+HRsyjZq+WaKwt2Np5AlpbK54O/pSbG4+QlZ7JqY9/JO7cTSzsbei+\nYxoRB87libNINBo8Ph7BjUFTyIyMofZvX5Cw+w/SQm/nmKRcuE5M73eRqWm4DOyObvyr3B79GQDV\n5rzD3fk/oz8UgsbOBplt/meRhUbgP30IG1+ahT4ilhe3fMz1oJPEmehu0N+f1PgkVrUfS53efrSd\n2J8dI+eRmZbB0dm/4PpENVyfqJZjn5GUyk/dJuX8/+LWaVzbcdxsTWbzL59FpvknEhFCtAaeBppJ\nKRsDXYDbDz8q59ginaCUclNJOJe/i3dgcy6sPwRAxOlrWDvZY1+5Yh4b+8oVsXKwJeL0NQAurD+E\nd1dfw/EBzbnwy0FD+C8H8Q70LVW9vj6NqODkWKppPKBDt3ZsW7cDgPOnLuLo5IBrZdcCdg+ci9ZC\ni4WlJVLm3jje/Wg0c6ctyBP2KOoFNidkgyFPw06HYuNoh4Nb3jJxcKuItaMtYadDAQjZcJD6xrxv\nObALB77dRFa64cJOikkwO+0H1A1oztn1Bg13Todi42SHQ7564VC5ItYOttwxaji7/iBPBDYHIF2f\nkmNnZWddrPN/gGvT2iTejEJ/6y7ZGVn89dtRqndtnsemWtdmXF9n0HlryzGqtGsAgJRgYWeN0GrQ\n2liRnZ5Jhj6F1Oh44s7dBCAzKZX7oeHYubuYpceuSR3S/4og43YUMiOT+5sP4BTQKo9N0tFzyFTD\nJ+CTT1/GUmeoL9be1RFaLfpDIQBkJ6fm2JlDFZ/axN+MIsGYF1c2HcUrMG9eeAY245LxWgzdeoxq\nbQ15kZmSRsTxK2SmFb3oZEVPHbaVnAj/47LZmsxGSvO3csg/4mAAd+CelDINQEp5T0oZLoS4KYSo\nBCCE8BVCBBt/TxVCrBJCHAZWCSGOCiEaPIhMCBFstH9FCDFPCFFBCPGXEEJj3G8vhLgthLAUQtQW\nQuwQQpwUQhwUQjxptPEUQhwRQpwTQkz/OyfloHMmMSIm5//EyFgcdM4FbPSRsYXa2FVyIik6HoCk\n6HjsKuU+LXs082bwjhn0W/E+rnWr/h15ZYqbzo3I8NwujKiIu1R2r1So7Tdr5hB0bjPJ+mT2bAkG\noEPXdkRH3uXqxWvFStexijP3w3PzOyEyFqd8ZeKkcyYhItfmfkQsjlUMNpW8dNRs+QSvb/yYYWun\nULWxV46dc3U3Rm6dybC1U6jZ4omiNehcSAjPrRcJkbnxm+pMMKkXCRGxOOpyb9Yd33+eMUfm0rBv\nG/Z/kduVVK2ZN8O3z2TAinG41Sm6XtjqnEk2yYfkiFhs3fNqsNM5k2S0kVnZZCQkY+3iwK0tx8hM\nTuPZkHk8c/wr/ly4jfT4pDzH2lerhEvDmtw7ZV75WOhcyYi4l/N/RmRMjgMpDJcXA0jcfxIAa8+q\nZCUkUePbCXhv+QrdhFdBY/6ty17njN4kL/QRhV+niSZ5kZ6YjI2zeR0adXr7cXXzUbP1FIvsbPO3\ncsg/5WB2AdWFEFeEEAuEEB3MOKY+0EVKOQBYC7wAIIRwB9yllCceGEop7wMhwIN4nwZ2SikzgEXA\naCllc+A9YIHR5mvgWyllIyDisc+wBIk6f5NFrd9mZbdJnFq+i76L3ylrSaXK6AFj6ebTFytrS1q0\na4a1rTWvjhnEws+W/uNaNFotthUc+K7vB+yY+SP9548BIDE6ns/bjGFBz4lsn7aaF74ehbWDbanp\n2Pf5Oua2HsP5jb/TYkggABHnbzK3zVss6j6R48t38vzid0sl7UpNvZBZ2WxoOpqNrd6l3ogeONRw\ny9lvYWdN+yVvcfKD1WSatLZKiop9/bFt5M29RRuMCWqwb1GfiJnLCO3zLlbVdTg/17nE0/271O3d\nmiu/HSmdyJWDeTRSSj3QHBgO3AXWCiFeecRhm6SUD2rvz8Bzxt8vAL8UYr8WeNH4u78xDQegDbBO\nCBECfIehNQXQFlhj/L2qKBFCiOFCiBNCiBNH9VfxGdyFwdtnMHj7DJKi43F0z30Kc9S5oI+My3O8\nPjIOB5MnU1Ob5HsJOV1q9pUrknzP0B2Trk8hI9nQBXBj3xk0FlpszXyaKkuef+UZfghaxg9By7gX\nHYPOo3LOvirubkSbPMHmJz0tnf07D9Ghazuq1ayKRw131uz5nk3Hfqayuxs/7FqKq1vh3TGtBgXw\n5raZvLltJvroeCp45No56VxIyFcmCZFxOJl07VRwdyExymBzPzKWizsNfel3zlxDZkvsXBzJSs8k\nJV4PQPj5G8TeisLVU5cTh+/gAF7bNpPXjBqcPHLrhZMuN/4HJEbF4WRSL5zcXUg0adE84NzGwzzZ\nvYUhj0zqRei+M2gfUi9SIuOwM8kHO3cXUiLyakiOjMPeaCO0Giyd7EiL1VPrmTZE7DuLzMwiLSaB\nu8ev4NLE0JITFlraL3mLmxt+5/b2E5hLZmSMYYDeiKXOlYzImAJ29m2b4PbmC9x8bTrS2E2ZERFD\nyp83yLgdBVnZJAQdxbZhbbPTToqMw8EkLxzcC79OHU3ywsrRjtQ4/SPjrlSvBsJCw11j12GJI7PN\n38oh/1QLBilllpQyWEr5ITAK6AdkmmiwyXdIksmxd4AYIURjDE5kbSFJbAK6CSFcMDizvca446WU\nPiZbPVNZZuheJKX0lVL6+jnUIWTlblZ2n8TK7pMI3XmSBv3aAeDetDZpick5XV45JxEdT7o+Bfem\nhguiQb92hO4yNP2vBZ2iwXPtDeHPtSc0yBBu55a73LiuiRdCI0gxo7KXNeuW/8rAgKEMDBhK8PaD\n9Hi+GwANm9VHn6gnJjrvDcXWzjZnXEar1dK2c2tuht7i2qXrBDbqTe+WL9C75QtER9xlYOAwYu4W\nvAED/LEqiPk9JjK/x0Qu7jqBz7OGPK3W1Ju0xBT0d/OWif5uPGmJKVRr6g2Az7Pt+dNYJn/uOoGX\nX30AXD11aC0tSI5NxM7FEaExLMvvXL0yrrV0xN3K7QI8sTKIxT0msrjHRC7vOkHjfgYNVZt6k5qY\ngj5fvdBHx5OmT6GqUUPjfu25Yix/l1pVcuyeCGxOzDVDA9vepF54PKJexIRcx9FTh311NzSWWmr2\n8SNs16k8Nnd2ncLreYPOGk+3JOrQRQCS7sTkjMdoba2p1MybhFDDgLjfnP+QcDWcS4u2F5puUSSf\nvYp1LQ8sq1VBWFpQoddTJOw+lsfGpr4XVWe8yV+vTSMrJnfGYMrZq2id7NG6GLqQ7Vs3JvXqLbPT\njjpznYq1dDgZ86Jubz9uBOXNixtBp3jSeC1692xJ2OGLZsVdt09rrpZW6wUgK8v8rRzyj8wiE0I8\nAWRLKa8ag3yAvwBbDM5gOwaH8zDWAuOAClLKs/l3Sin1QojjGLq+tkgps4AEIcQNIcTzUsp1QggB\nNJZSngEOY2jprAYG/p3zur56nMp/AAAgAElEQVQ3BM+OTfjPwTlkpKSz471FOfsGb5/Byu6GWSa7\nJy+n+5zhxmnKZ7ix7wwAfyzYTK9vR9PoxQ4k3LnH5je+AeCJHi1pMqgz2ZlZZKZmsGXU/L8jrwDv\nfziL46fPEh+fQOe+LzNy2CD69epaInHn5/CeI7Tt7MfGIz+RmpLKR+98krPvh6BlDAwYiq2dDV+s\n+AQrKys0GsGJw6dZv/K3x0r3yr4Q6nb04d39X5KeksaG97/L2ffmtpk5s8A2TVlGv9kjDNOUg89w\nJdgwgHzq52Ce+ex1Ru/8lKyMTNaP/RaAWi2fpPO7z5OdmYnMlvw2aRkp95MKCgBC94bg3dGHNw98\nQaZxmvIDXts2k8VGDdsnf09v4/T1a8FnCDXWi07j++Pq5Y7Mlty/c49tEw0zyOr1aInvy13Izswi\nIzWDDaPnFZkPMiubE5NW0OnHcQithms/7ef+lTs0fr8fMWducGfXKULX7KfN3BH0PjyHtHg9h98w\nxHfl+yD8vhxOz32zEEJwbe0B4v+8jVvLung93564i7foHjQDgDOf/Ez43jOPLpisbMI/XIjnyo8M\n05TX7Sbt6i0qvzOQlHNXSdx9DPcJr6Kxt6HG/PEAZITf5a/XpkN2NpEzl+H5w3QEgpTz14j7adej\n0zTJi/1TVtB79Tg0Wg0X1+4n9sodWo3tR/TZG9wIOsXFn/YT8NUIBh005MWON3PzdsjvX2LlaIvG\n0gKvrr5sHDgrZwaa99Ot2Dzkc7O1FJty2vVlLuLvzFApdiJCNAe+ASpiaLWEYuguqwcsBRKAYMBX\nSukvhJgK6KWUs03iqALcAaZJKT8yhr1iPGaU8f/ngHWAv5RyvzHME/gWQ9eYJfCTlPJjY/iPgAPw\nG/D2o6Ypz67xcplP1VAfHMtFfXAsF6+MstcA6oNjpoy+vfqxCyVl6Xtm33Nsh80uH5XAhH+kBSOl\nPIlhLCQ/B4G6hdhPLSQsinx6pZTLgeUm//8CiHw2N4BuhcR3A2htEjS56DNQKBSKMqCcjq2Yi3qT\nX6FQKMopxXmhtDyiHIxCoVCUV/7lYzDKwSgUCkV5pZzODjMX5WAUCoWivKJaMAqFQqEoFZSDUSgU\nCkWpUE4XsTQX5WAUCoWivPIvb8H8Y0vFKBQKhaKYZEvzt0cghOgmhLgshAgVQowvwuYFIcRFIcQF\nIcSPjytftWCKQYoo++ZqeXiDHuDIuRVlLaHc5MU7omZZSyBWW9YKDDS9c+rRRqXMEreOZS2h5Cih\nWWRCCC0wHwgAwoDjQohNUsqLJjZ1gAlAWyllnBCicuGxmY9yMAqFQlFOkSXXRdYSCJVSXgcQQvwE\n9AFMV/V8DZgvpYwDkFKa/03qIlBdZAqFQlFeKUYXmemnRYzbcJOYqpL3K8JhxjBT6gJ1hRCHjR95\nLLDEVnFRLRiFQqEorxRjLTIp5SIMH1j8u1gAdQB/oBpwQAjRSEoZ/9CjHoJqwSgUCkV5peQG+e8A\npsuPVzOGmRKG4UOPGcbFgK9gcDh/G+VgFAqForySmWX+9nCOA3WEEJ5CCCsM38LalM9mI4bWC0KI\nShi6zK4/jnzVRaZQKBTllRJarl9KmSmEGAXsBLTAMinlBSHEx8AJKeUm475AIcRFIAt4X0pZ8LvW\nxUA5GIVCoSivlOBy/VLKbcC2fGEfmPyWwLvGrURQDkahUCjKKSU4TblMUA5GoVAoyivqg2P/v+k6\ndTDeHZuQkZLOpve+I/L8zQI2uoa16DNnBBY2loTuO8POqSsB8B/7HHUDmiOzJUkxCWwauxB9dDw1\n/erxwuJ3ib99F4BLO45zcO6vZmt6b9pbtO3sR2pKGlPfnsnlc1cK2Mz9cTaVKruitdAS8scZPp3w\nJdkmT0sDX3+Rd6aOonODp7kfW7LfWZ888wsOHD6Gi3NFNq5eWKJx56cs8sLdvzG+0wYhNBpC1wRz\ncd7mPPs1Vha0mTsCl0aepMUlcmjEPJLC7iEstPjN/g8ujWohLDTcWHeIC/M2o7G2JGDDZLRWFggL\nLbe2HuPc7A2P1FHDvzFPTR2E0Gq4uCaYkwsK6gj8agRujTxJjUtkx8h5JIbdw6aiA92/G0PlJl5c\nWneA/VNW5hxTp09rfEf1BilJiopn15gFpMbpH6nlAV9+8THdu3UiOSWFYcPe4XTI+QI2e4LWoXOv\nQkpKKgDdewzg7t0YBg96gU9nTeZOeCQACxZ8z7Lv15iVblX/xrT82FAmV9cEc25+wbxo//UIXI1l\nsv+NeejD7gHgXK86rT8diqWDLWRLtvT8gKy0DDSWWlpNH4KuTT3Ilpz6dB1/bTtudl6YhXIwpYMQ\nIgs4h0Hjn8AQKWVyEbZTAb2UcvY/pxC8OzbBxVPH/A5jqdrUmx7TX2VZ3w8L2PWYMZQt45dw53Qo\nA1aMo7Z/E64Fn+H377YSPOcXAFq80pWn3nqWbZOWAXDr+GXWDi3+6bTt5Ed1r2o802YADZvVZ8Ks\nsbzS8/UCdhOGf0CS3pCdny2ZRpdeHdn12x4AqnhUxs+/JRFhkcVO3xz69gjgpX69mTitdIurLPJC\naAQtZg5hb/9ZJEfE0m3bx4TtPEnC1fAcm9oD/EmPT2JT27HU7ONH08n9OTRiHjV7tURjbcHWzhPQ\n2lrxdPCn3Nx4hKSwe+x5fiaZyWkICy2BG6cQvvcMMaeuPVSH//QhbHxpFvqIWF7c8jHXg04SZ6Kj\nQX9/UuOTWNV+LHV6+9F2Yn92jJxHZloGR2f/gusT1XB9olpunFoNT019mR86/ZfUOD1tJvan8SuB\nHPvy0c4OoHu3TtTx9uTJ+u1o1bIZ8+d9Qpt2vQq1HTx4FCdPnS0Q/vO6Tbz19mSz0jPNi1YzhrBr\ngKFMnt72Mbd2neS+SV7UGeBP+v0kNrQbi2dvP5pP6s/+N+YhtBraz32Dg28tJO7iLaydHcjOyASg\n8Zg+pMYk8Gv790EIrCvaF0uXWfzLPzhWnqcpp0gpfaSUDYF0YERZC8pP3YDmnF1/EIA7p0OxcbLD\noXLFPDYOlSti7WDLndOhAJxdf5AnApsDkK5PybGzsrNGlsDS3B26tWPbuh0AnD91EUcnB1wruxaw\ne3BD1VposbC0zJP2ux+NZu60BSWipzB8fRpRwcmxVOI2pSzywrVpbRJvRqG/dZfsjCz++u0o1bs2\nz2NTrWszrq8z1JtbW45RpV0DwLAyu4WdNUKrQWtjRXZ6JhnGOpKZnAaAxlKLxtICHiGnik9t4m9G\nkWDUcWXTUbwC8+rwDGzGpV8MOkK3HqNaW4OOzJQ0Io5fITMtI4+9EAIhBJZ21gBYOdiSFBVnVr4A\n9OrVlVU/GB6o/jh2igoVK6DTPfZyV4+kUr4yufHbUWrkK5Magc0INZbJza3HcDeWiUeHRsT9eZu4\ni7cASIvTI42tijr9O3DuG2NLSErSitGSMxeZLc3eyiPl2cGYchDwBhBCDBZCnBVCnBFCrMpvKIR4\nTQhx3Lh/vRDCzhj+vBDivDH8gDGsgRDimBAixBhnsV4qctS5kBCeO4svITIWxyrOeW2qOJMQGZtr\nExGLo84l5/+O7z/PmCNzadi3Dfu/+CUnvFozb4Zvn8mAFeNwq5N/RYeicdO5ERmeu4RQVMRdKrtX\nKtT2mzVzCDq3mWR9Mnu2BAPQoWs7oiPvcvVi0U/H/xbKIi9sdc4kh+eWd3JELLbueeuEnc6ZJKON\nzMomIyEZaxcHbm05RmZyGs+GzOOZ41/x58JtpMcnAYan8O5BM+h3dgERB84Rc/rhmux1zuhNdOgj\nYnHQ5dXhoHMm0URHemIyNs4ORcaZnZnFvonf81LQLIaemIdL3apc/Cn40ZlipKqHjrDbua2GO2ER\nVPXQFWq7ZMkXnDi+i0kT384T/uwzPTh1Moi1Py2iWjUPs9I1zW+ApIhY7HQPL5P0hGSsnR2o4KUD\nJAE/jKPXjuk0fKMnAFZOdgA0HfccvXZMx/+70dhUcjJLT7EowdWUy4Jy72CEEBZAd+CcEKIBMBno\nJKVsArxVyCEbpJQtjPv/BIYZwz8AuhrDexvDRgBfSyl9AF8Mb7LmTz9nfZ8T+tASPTeAfZ+vY27r\nMZzf+DsthgQCEHH+JnPbvMWi7hM5vnwnzy8usVmDeRg9YCzdfPpiZW1Ji3bNsLa15tUxg1j42dJS\nSa88Ux7yolJTL2RWNhuajmZjq3epN6IHDjXcAMOT7PaASfzafAyuPrWpYNJ19U+hsdDSaFAX1nSf\nxDLfUcT8eYvmo3o/+sBiMmjIaJo264J/x2do17YlL7/8HABbtgZRu44fzZoHsHv3Ab5f+lWJp50f\nodVSuUVdDoxawLa+H1Ojuy/u7RogtBrsPVyJPnGFzd0mE30ylBYfvFTyArKzzd/KIeXZwdgKIUKA\nE8AtYCnQCVgnpbwHIKWMLeS4hkKIg0KIc8BAoIEx/DCwXAjxGoYXjQCOABOFEP8FakopU/JHJqVc\nJKX0lVL6+jp44zs4gNe2zeS1bTPRR8fj5JHb5eKkcyExX5dBYlQcTiYtFid3FxIjC8o+t/EwT3Zv\nARi6zjKMXSKh+86gtdBi+5Any+dfeYYfgpbxQ9Ay7kXHoPPI7Xao4u5GdMS9Io9NT0tn/85DdOja\njmo1q+JRw501e75n07Gfqezuxg+7luLq5lLk8eWNss6LlMg47DxybezcXUiJyFsnkiPjsDfaCK0G\nSyc70mL11HqmDRH7ziIzs0iLSeDu8Su4NPHKc2xGQjJRv1/Eo2Pjh+pIiozDwUSHg7sL+si8OvSR\ncTia6LBytHvogH2lBobPEiT8ZWgVXt3yB+7NH97of2PEEE4c38WJ47uIiIyiWvXcVkfVau45A/am\nhBvD9Pok1vy0kRa+PgDExsaRnp4OwNJlP9KsWaOHpv0A0/wGsHd3ITny4WVi5WRHWpye5IhYov64\nTFqcnqzUdML2nsGlYS3S4vRkJKfy17YTANzc8gcuDWuZpadYqBZMqfFgDMZHSjlaSplu5nHLgVFS\nykbAR4ANgJRyBIbWT3XgpBDCVUr5I4bWTAqwTQjR6VGRn1gZxOIeE1ncYyKXd52gcb/2AFRt6k1q\nYgr66Lzrwumj40nTp1C1qTcAjfu150rQSQBcalXJsXsisDkx1yIAsHerkBPu0cQLoRGkPOTCX7f8\nVwYGDGVgwFCCtx+kx/OGRVAbNquPPlFPTHTel3Ft7WxzxiK0Wi1tO7fmZugtrl26TmCj3vRu+QK9\nW75AdMRdBgYOI+ZuYX68fFLWeRETch1HTx321d3QWGqp2cePsF15v5FyZ9cpvJ431JsaT7ck6pBh\nxfSkOzE54zFaW2sqNfMmITQcaxdHLI1dMlobS9yfakRCaDgPI+rMdSrW0uFk1FG3tx83gvLquBF0\niiefM+jw7tmSsMMXC4sqh6TIWFzqVMXGxTB+Vr19I+IeoePbhSvwbRGIb4tANm3ayaCBhtZIq5bN\nSLifQGRk3hXhtVotrq6G7isLCwt69uzChQuXAfKM1/TqFcilS+b1KNwLuY6Tpw4HY1549vHjdr4y\nub3rFN7GMqnVsyURxry4s/8szk9WR2tjhdBq0Pk9yf2rhiW8woJOG2aQAR7tGuSElyj/cgdTbmeR\nFcFe4FchxBdSyhghhEshrRhHIEIIYYmhBXMHQAhRW0r5B/CHEKI7UF0IUQG4LqWcK4SoATQ2pmEW\noXtD8O7ow5sHviDTOE35Aa9tm8niHhMB2D75e3rPeR0LGyuuBZ8hdN8ZADqN74+rlzsyW3L/zj22\nTTTMIKvXoyW+L3chOzOLjNQMNoyeZ3YGHd5zhLad/dh45CdSU1L56J1Pcvb9ELSMgQFDsbWz4YsV\nn2BlZYVGIzhx+DTrV/5mdhqPy/sfzuL46bPExyfQue/LjBw2iH69upZ4OmWRFzIrmxOTVtDpx3EI\nrYZrP+3n/pU7NH6/HzFnbnBn1ylC1+ynzdwR9D48h7R4PYffMJTvle+D8PtyOD33zUIIwbW1B4j/\n8zYV61Wn9devIzQahEbw1+Y/uLM75JE69k9ZQe/V49BoNVxcu5/YK3doNbYf0WdvcCPoFBd/2k/A\nVyMYdNCgY8ebufVsyO9fYuVoi8bSAq+uvmwcOIu4q+Ec+2oD/X6ZTHZmFolh99j9rvmL927bvodu\n3Tpx+c/DJKek8J//5Hb9nji+C98WgVhbW7Ft649YWlqg1WrZs+cgS5b+AMDoUUN5+ulAMjOziIuN\nZ+h/3i4qqQJ5cXTyCgJ+HGeYOr52P/FX7uDznqFMbged4upP+2k/dwTPHjLkxf6RhrxIv5/MhUXb\neXrbxyAlYXvPELbHkPcnZvxE+7lvYDX1ZVJjEzn8zuMsZFy09n8zorRmCj0uQgi9lLJAv5AQYgjw\nPoa1ck5LKV8xnaYshHgDGAfcBf4AHI02GzCsDCqAPcDbwH+BQUAGEAm8VES3GwDTag4s88z6Lf1W\nWUsA1BctTVFftMzlnah9ZS2h3HzR8pU7q8XjxpEwLMDse47T0qDHTq+kKbctmMKcizF8BbAiX9hU\nk9/fAt8WctyzhUQ3y7gpFApFuaO8Tj82l3LrYBQKheL/PcrBKBQKhaJU+HcPwSgHo1AoFOUVmfnv\n9jDKwSgUCkV55d/tX5SDUSgUivKKGuRXKBQKRemgWjAKhUKhKA1UC+b/EW7ZZf8eUzfr6mUtASgf\nLzmWh5c9AZ5rNqasJVBRY13WEgD4wdW/rCWwSmv+JwRKk1dKIhLVglEoFApFaSAzy1rB46EcjEKh\nUJRTpGrBKBQKhaJUUA5GoVAoFKWBasEoFAqFolRQDkahUCgUpYLMKvuZq4+DcjAKhUJRTlEtGIVC\noVCUCrIcvHv3OCgHo1AoFOUU1YL5f0x1/8a0nToIodXw55pgQhZszrNfY2VBp69G4NbIk9S4RHaP\nnEdi2D0q+3jx1KxhBiMBJ778lZs7TuQcJzSCflunkRQZx/ZX55ilpeeHg6nb0YeMlHTWv7eQiAs3\nC9h4NPTk2dmvY2ljxZV9IWz9aGXOPr8hgbQaHEh2VjZX9p5m56w1VKxWibd2z+be9XAAbp8OZdOk\nZWbnz3vT3qJtZz9SU9KY+vZMLp+7UsBm7o+zqVTZFa2FlpA/zvDphC/Jzs69qga+/iLvTB1F5wZP\ncz/2vtlpP4rJM7/gwOFjuDhXZOPqhSUWb2G89tFwmnf0JS0lja/HfsX189fy7Leysea/345HV1NH\ndnY2x3cfY+UswyoF3V7uTvfBPcnOyiY1OYUF4+dx++rtv6Vj4IdDadKxGekp6Sx+7xv+unCjgE2/\n916i7bMdsK9gz+sNXs4Jf2nKKzzZuiEA1jbWOFaqwMjGgx+Zpq5jY5p+bLhGrv8YzKV5Ba+RVnPf\nwLlxLdLj9Pz++jckh91DY6nF97NhODfxguxsTk1Zxd0jf2Jhb0OnjR/kHG/n4cJf6w9x+oPVxcqL\n4R+9jq+xTL4a+yXX8pWJtY0147+dkFMmx3YfY8Ws5QB0fq4LQycNJSYyBoAtKzaz66ddxUrfXKRU\nLZhygxCiL/ArUE9KealU09II2k0fwpaXZpEUEcuzWz7mr6CTxF0Nz7Gp19+ftPgk1rQfS+3efrSa\n2J/dI+cReymM9T2nILOysatcked3zuCvoFPILMONtdGwbsSFhmPlYGuWlrr+Prh66vjS/12qNfWm\n94yhfNf3gwJ2vacPZeOEJYSdDmXw8nHU8W/C1eAzeLauT70AX+Z1H09Weib2rk45x8T+FcX8HhOL\nnT9tO/lR3asaz7QZQMNm9Zkwayyv9Hy9gN2E4R+QpE8G4LMl0+jSqyO7ftsDQBWPyvj5tyQiLLLY\n6T+Kvj0CeKlfbyZOm13icZvSvKMv7rU8GPHUcOo2fYI3Zozk/T5jC9htXLSBc0fOYWFpwcdrZtDM\nvzmngk+yf2MwO1ZvB6BlQEuGTvkPHw3+sNg6Gvs3Q+fpzjj/UdRuWochM4bzcd8JBexC9hxn94pt\nfBY8L0/4j9OW5/zuMqQ7NRt4PjJNoRE0n/kKwS9+QkpELAHbpxG+6xQJV+7k2HgN8Cf9fhLb2oyl\neh8/mkwewJER3+A1sBMAOzuNx9rViad+HEdQtylkJqWyKyC3PgbsnE7YthMF0n4Yvh198ajlwfCn\nXuOJpk8wcsabjO3zbgG7DYs2cO7IWSwsLZixZgbN/ZtzMvgkAAc3H2DhB6X7YAL//haMpqwFlDAD\ngEPGv6VKZZ/aJNyMIvHWXbIzsri26Si1ApvnsakV2IwrvxwE4PrWY1Rt2wCAzNT0HGeitbZEmqxn\nZ69zoUYnH/5cE2y2lnqBzQnZYEgn7HQoNo52OLhVzGPj4FYRa0dbwk6HAhCy4SD1A30BaDmwCwe+\n3URWumFdiqSYBLPTLooO3dqxbd0OAM6fuoijkwOulV0L2D1wLloLLRaWlkiTzHj3o9HMnbYgT1hJ\n4evTiApOjiUeb35aBrZi3/q9AFw5fRl7J3ucKzvnsUlPTePckXMAZGZkcv38NVzdKwGQok/JsbO2\ntfnbedEssAWHN+wH4Nrpq9g52lMhXx15sO/+3fiHxuXXux1HNx16ZJouTWuTeDOKJOM1cuu3o1Tt\nmvca8ejWnJs/HwAgbMsxqrQ3XCNOdasSdfgiAGkxCWTcT8KlSV6n5uClw8bVibtHi/cs2SrQj73G\nMrlcRJmkpaZx7shZwFAm185fo5KxTP5JsrOE2Vt55H/GwQghHIB2wDCgvzFMI4RYIIS4JIQIEkJs\nE0I8Z9zXXAixXwhxUgixUwjhXpz07HXO6MNjc/7XR8Rir3Mu0kZmZZOemIyNswNgcFAv7J7FC0Gf\ncGDi9zkOp83Ulzk6c02xvsXtWMWZ+yZaEiJjccqnxUnnTEJErs39iFgcqxhsKnnpqNnyCV7f+DHD\n1k6hamOvHDvn6m6M3DqTYWunULPFE2ZrctO5ERkenfN/VMRdKhdxgX6zZg5B5zaTrE9mz5ZgADp0\nbUd05F2uXrxW6DH/Flx1rtyLuJfz/73IGFx1BR3tA+yd7GnRpSVnD4fkhPUY3JOFBxfzysRXWfzh\nor+lw7mKCzHhuTpiI2NwfoiOonCt6oZb9Spc/P38I21tdS6k3InJ+T85IhbbfPXSTudMssk1kpGQ\njJWLA/EX/6JqYDOEVoN9dTecG3tiVzWv3hp9WnNr09Hin4POlXsRd3P+j4m898gyadmlFSGHz+SE\ntenRlm92zmPCwgml6nhktjB7K4/8zzgYoA+wQ0p5BYgRQjQHngVqAfWBQUBrACGEJfAN8JyUsjmw\nDJhRWKRCiOFCiBNCiBMH9VdLTGx0yDV+7jKe9U9/QLM3e6G1tqRGZx9SYxK4d+5miaVjDhqtFtsK\nDnzX9wN2zPyR/vMNqwMnRsfzeZsxLOg5ke3TVvPC16OwNrPbrjiMHjCWbj59sbK2pEW7ZljbWvPq\nmEEs/GxpiadVntFoNYz95n22fL+JqFtROeHbVm5lRPvXWPHJcl4Y82IZKoRWvdpyfNsRZHbp9t3c\nWLOf5IhYAnZMp+nHg7h34mrOQ9gDavRtza2Nv5eqDo1Ww/vfjGPT95uIumXoqj22+w+GtnmV0V1H\ncfrgad75omD3WklRkg5GCNFNCHFZCBEqhBhfyP4RQohzQogQIcQhIUT9x9X/vzQGMwD42vj7J+P/\nFsA6KWU2ECmE2Gfc/wTQEAgSQgBogYjCIpVSLgIWASys/nJOsyIpMg4HD5ccOwd3F5Ii8y4T/sAm\nKTIWodVg5WhHapw+j018aDgZSam4PFENnW9dagY0o0bHJmitLbF0tKXT12+w961vC+hqNSgA3wEd\nAbhz5joVTLQ46VxIyKclITIOJ/dcmwruLiRGGWzuR8ZycedxY1zXkNkSOxdHkmMTSUk36A0/f4PY\nW1G4euoIP1dwcBjg+Veeoe/AXgBcPHMJnUdlHjzzVXF3I9rkST4/6Wnp7N95iA5d23EvOhaPGu6s\n2fM9AJXd3fhh11KGdB9OzN3YIuMoL/QY3JOAAV0BCD17Nc8TbiWda87gcH7enDWaiJvhbF66qdD9\nBzcdYMSMkWbr6DyoGx0GdAHgxplQXD0q8eARyUXnSlwROh6GX6+2rJyyxCzblMhYbE1aHXbuLqTk\nq5fJkXHYebiQEmG4Riyd7EiPNdS5kA9zB+47b/qQxOu5Y3EV69dAo9UQd/amWVp6Du5J1wHdALh6\n9gqV3N1y9rnqKhVZJqNnjSb8Zjiblv6WE5YYn5jze9eaXbw6YahZGv4OJdU7LITQAvOBACAMOC6E\n2CSlvGhi9qOUcqHRvjfwBdDtcdL9n3AwQggXoBPQSAghMTgMiWHAv9BDgAtSytZ/N83oM9epUEuH\nY3U3kiJjqd3bjz2jF+SxuRl0irrPtSfqVChePVsSbuxTdqzuhj48BpmVjUNVVyp6e5B4+y7HPv2Z\nY5/+DICHXz2avN6jUOcC8MeqIP5YFQRA3Y4++A0J5OymI1Rr6k1aYgr6fP3o+rvxpCWmUK2pN2Gn\nQ/F5tj1Hlxtmvvy56wRefvW5ceQirp46tJYWJMcmYufiSEq8Hpktca5eGddaOuJuRRfQ8oB1y39l\n3XJDlrft3JoXhj7Lzo17aNisPvpEPTHReS9iWztb7Bzs/o+98w6Pqmj78D27m94TyoYeQlFaKKEK\nGKSjFMWCCKKoCIL6KkVBREUFbICAiqhYUMGXIlJCVUJHSugIIYQAqaSSvsnuzvfHLkk2CZBAIsn3\nzp3rXNeeOc+Z+e2czXnOzDxnhqSrSWi1Wu7r2Zljf5/gwtkI+rQclG+37uB/GdnvhXKNIqtIgn/a\nSPBPGwFo90AgD456iN3rdtGkTVMy07NIuVp8vZKnJo3A2c2ZRVMW2KT7NqhFbKQlcCSwZ/v8z6Xh\nz2Wb+XOZZRwsoEdbeo3qz4F1e/Bv05js9KxbjrUUxde/Ns4eroSHniuVffKxCNz89LjUrU52XDL1\nBndi/0tf2NjEbAmlwS0lL0UAACAASURBVOPdSToSTp2HOhC/5zQAWid7QGDKNlCzewvMJrNNcEC9\nIZ25tHZ/qbVv/GkjG63XJPCB9jw06iF2rdtJ0zZNyUrPLPGajJg0Emc3FxYUuSZeNbzy7Tv27siV\n8NuL6isN5dj11QEIl1JGAAghVmDp9cl3MFLKwoOvLljuoXfE/wsHAzwKLJNS5ocpCSF2AsnAUCHE\nj0B1IAj4FTgHVBdCdJZS7rd2mTWRUp4ubYHSZGbP2z/y4M9TEFoN537bSUpYNIETh5Jw4iKXtoVy\ndsVOHpg/lid3f4YhNYNt4y2ROfr2TWjz0kDMRhPSLNn91g/FWjZlIWzHMZr0aM3rO+eRm21gzeSv\n84+ND56VHwW27u2lDP10rCVMOeQ4YSGWfv7Q/4bw8Mcv8vKWjzDlGVk90eLUGnS4h56vP4bZaESa\nJX+8tZTsa5ml0rT3z/3c17MTa/evICc7h/dem51/7JdtS3mq92icnB2Z++Ns7O3t0WgEh/ceZfVP\nf9wk1/Jj8jtzOHT0BKmpafQcMoKXnhvJ0IF9y72cI38dJrBHIIt3f4Mh28DCSfPzj83btIDX+r+C\nj96Hx18ZxpXzV5gbbGmEB/+4gW0rtvLgMw8R0DUAY56JzGsZzH993m3pOL4jlFY92vLJzi8wZBv4\ndnLBjX5m8KfMGDAJgMffHEnnwd2wd3Jg3v4l7PxtO2vnWx56Og68j7/X7y11mdJkJnTaD9y//A1L\nmPKKnaSFRdNi8lCSj18kZmsoEctD6LRwHAP2fUZuaib7xy4EwMHHnfuXvwFSkhWbwt8v2z5o1R3U\niV0jPr6tujj81yECewTyze5vLWHKkwrqdMGmhbzS/2V89D4Ms16Tz4MtDuZ6OPKgZwfRoXdHzEYT\n6akZzJ94e9ekNJRjmHJtoLAnjAI6FjUSQowHXgfssTy03xGiIiJ0/m2sXV8fSSk3F0p7BbgXS2sl\nCEvlCqvdNiFEa2AB4IHF0c6XUn5zs3IKd5HdLaK0lSNucbOh4p7aSota0bKAyrKi5QCD492WwDK7\nsrXMKooNlzfesXcIu7dfqe85Tc9ueREYUyhpibWLH2twUz8p5fPW/ZFARynlhJLyEkIMB/pKKe9o\n6dr/Fy0YKWWPEtIWgCW6TEqZIYTwAQ4CJ63HjwHd/1WhCoVCUQbK0oIpPF5cAtFA4fXW61jTbsQK\noOT++TLw/8LB3IINQghPLE2+96WU5f/WnkKhUFQA5TgGcwhoLITww+JYhgHDCxsIIRpLKa/HgTwI\n3HHY7P97ByOlDLrbGhQKheJ2KK8RDCmlUQgxAdiCJQhqqZTytBBiJnBYSrkOmCCE6AXkASnAHXWP\nwf+Ag1EoFIqqSnm+QCmlDAaCi6TNKPT51XIrzIpyMAqFQlFJMZmr9rvwysEoFApFJaWqB/kqB6NQ\nKBSVFLOarl+hUCgUFYFaD0ahUCgUFYLqIvsfIkZ796+2QyV5onlN1L/bEirFG/QAq0IX3Nqogkkb\n9ezdlgBA6GH93ZbAqvXj77aEckN1kSkUCoWiQlBRZAqFQqGoEO5+n8mdoRyMQqFQVFJUF5lCoVAo\nKgQVRaZQKBSKCqFyLM5x+ygHo1AoFJUUiWrBKBQKhaICMKouMoVCoVBUBKoF8z/OgHeepnGPAPKy\nc/l90tfEno4sZuPbogGPfDoWnaMd53ccJ/i9nwDo8Z9HaDesB5nJ6QBs//g3zoccR6PTMvij56nV\n3A+NTsOxNXvY/eW6m+ro++7TNLLqWDfpa+JOFdehb9GAwZ9ZdITvOM6Wdy06giY+SpPe7ZBmSWZS\nGusmLibjair1O93L49+8TuqVBADObj7E7gW/l1i+b1ArAt8fidBoCF8ewplF622Oa+x1dFkwFu+W\nfhhS0tkzdhGZUYkInZZOnz6Pd8sGCJ2Giyv3cHrRejQOdvReMx2tvQ6h03J540FOfrrmpnVQEi+8\nN4Z2PQIxZBv4fOJ8Ik5dsDlu7+jAG1+9ib6+HrPZzKHtB/lpjmUp5n4j+tP/6Qcxm8zkZGXz5ZuL\nuHK+fJeKnj5rLrv2HsTby5O1Py8u17wLY9euAy5jXgaNhpytG8lZ+WuJdvZduuP21vukvjoGU/g5\nhJs7btNmomvcFMP2zWQu/vy2Nfj0CKDpB88gtBqif/mLyIV/2Bz37HQvTd8fhWuzepx88XOubvg7\n/5hjbR+azX0Rh1rVQEqOPjWHHOvvsqzsPfYPH33/O2az5OGeHXluSC+b47GJKUz/4lfSM7Mxm828\nOvwhurVtRp7RyMwlKzlz4QoajWDKMw/Tvnmj29JQWtQYzL+EEOItLCuwmbDU+4vAC8BcKeUZIUSG\nlNK1hPM6AZ8DDtbtNynlu+WhqXFQAD5+ej4PmkidNo0Y+OGzLBnyTjG7gR+M5o+p3xJ1NJyRP0yh\ncVAA50OOA7D/u03s/cZmiQaaD+iIzt6OL/q9iZ2jPRO2f8zJdftIjUosUUejHgF4++n54v6J1G7T\niAEfPMvSEnQM+HA0G978luij4Tz54xT8gwK4EHKcfV9vJOSzVQC0f6Yv3V99hOC3lgJw+dA5fhv9\n6U3rQWgE7WeN4q9hc8iKTaZf8Eyithwh7XxMvo3/k0Hkpmay7r6J1B/ciTbTh7Fn7CLqD+yAxkHH\nxp5T0TrZ81DIR0Su3U9mVCJ/PjYLY5YBodPSZ+3bxPx1nKTQCzdRYku7HoH4NqjF2O5jaNKmKeM+\nfInJgycWs1u7ZA0n959EZ6dj5vIPaRvUjtCQI+xcG8LmnzcB0KF3B0a//TzvPV28Xu+EIQN6M3zo\nIKa9f/M6viM0GlzG/Ye06RMxJybgMe9r8g7sxXTlkq2dkxOOgx8l7+zp/CSZm0vWsu/Q1vdDV9/v\nDjQI7pkzmtDHPyQnJomOW2aTsOUwmWEFq/bmRCdy+tUvqT9uYLHTmy8cz8X5v5O86yRaZwfkbc6h\nYjKbmfXdar6ePpaaPp4MnzqPoMAW+NcpmIHgm9Vb6du5NY/3uY8LUXFMmL2ETW1nsHr7AQBWfzaF\npGvpjJ+1hF9nv4ZGU3EvQ1b1FkyVeE1UCNEZeAhoK6VsBfQCrkgpn5dSnrnF6T8CY6SUrYEWwH/L\nS9c9fdpxbM1uAKKOhuPo5oxrdU8bG9fqnji4ORF1NByAY2t2c0+fdrfIWWLv5IBGq0HnaI8p14gh\nPfuG1k16t+PEaouO6KPhOLo741qjiI4anji4OhFt1XFi9W6aWnXkZhTkbX8b/7w+bfxJj4wn43IC\n5jwTl/44QN2+tt+xTt+2RKy0aLy84SA1uza3fFMJOmcHhFaD1tEec66RPKseY5YBAI2dFo2drsxv\nnXXo05Edq/8CIOzoOVzcXfCq4WVjk5tj4OT+k5by8oxEnLqAj281ALIL1YuDk+Nt39RuRmDrlni4\nu5V7voXRNbkXU0w05rhYMBox7PoLu05di9k5j3iO7FW/Qm5uQaIhB+OZk5CXW8y+LHi0bUTWxXiy\nL11F5pmIW7uP6v3a29jkXEkg48xlMNs+t7s0qY3QaUneZblOpiwD5uzb03Mq/DJ19dWoU7Madjod\n/bq0IeTQKVsjIcjIygEgIyuH6l4eAERExdGhhaXF4uPhhpuLE6cjyrdFWxRzGbbKSJVwMIAvkCil\nNABIKROllDFCiBAhROB1IyHEPCHEaSHEn0KI6tbkGkCs9TzTdYckhHhXCLFMCLFfCHFeCPFCWUW5\n1/TmWkxS/n5aXDLuetsbmLvei7TY5AKb2GTca3rn73cY1YeXNs1myMcv4OjuDMDp4IPkZhuYfPAL\nJu77nL3fbCT7WuYNdbjpvUkrosOtpq0Ot5pepMXZ6nDTF+joMfkxXtm/gBZDurBz7qr89DptGzFm\n0yye/HEK1RvXLrF8J70XWTEFeWfFJuPka1u+s96LTKuNNJnJS8vCwduVyxsOYswy8MixRTx8aD7/\nLA4mN9XyXYVG0H/bhww98SWxu06SdLT0rRcAH70PibEFrb7EuCR89D43tHdxd6F9rw6c2HssP23A\n0w+yePc3PDPtWb55Z0mZyq8saHyqYU68mr9vTkxA61PNxkbr3xhN9RrkHTpQIRoc9N4YCv1GDTFJ\nOBT5X7kRzv6+GNMyabV0Ih23z6HxjKdAc3tP9leTU9H7FDx81fDxID75mo3NuMf6snH3EXqPfZfx\ns5fw5uhHAGjSoBY7D5/GaDIRdTWJfyKuEJ+Yels6SosJUeqtMlJVHMxWoK4QIkwI8aUQ4v4SbFyw\nrC3dHNgJXO/LmAecE0L8LoR4UQjhWOicVsADQGdghhCiVgV+h2Ic/Hk787u/xlcDppF+NZV+058C\noE6AP2aTmU86TmBet9e47/kBeNWtfovc7owdn6xkQedXOLV2H+1H9QEg9lQkC7q8ypL+0zj0wxYe\n++b1ci+3WpuGSJOZNW1eZm3H17l37ABc61m+qzRLNvV+i9/bvYJPa388mtYp9/Kvo9FqmLhwMhu+\nX0f85fj89OCfNjK22wv8OPsHHn/liQor/64iBC7Pjyfr2y/vtpISEVotnh3v5fx7yzjYdxpO9WtS\na1hQhZW3ae9RBgW1Z9vid/li6hjeWvgLZrOZIT06UtPbg+FvzuWTH9YS0NSvQrvHAMyi9FtlpEo4\nGCllBtAOGAMkAL8JIZ4pYmYGfrN+/hnoaj13JhCIxUkNBzYXOucPKWW2lDIR2AF0KFq2EGKMEOKw\nEOJwaHo4HUb2ZlzwLMYFzyL9aioetQqeiN313qTFpdicnxaXgrtvQUvB3debtHjLk3xmYhrSLJFS\ncmTFDmoH+APQcnAXwneewGw0kZmUxuUjYdRq1dAm38Cne/NC8CxeCJ5FxtVU3IvoSI+31ZEen4K7\n3lZHeqEWzXVOrt3LPf0tXRe5GdnkWbupwnccR6vT4uRVbJiL7LgUnGsV5O3s6012rG35WXEpuFht\nhFaDnbszhuQMGjzchdgdJ5BGE4akNBIOheEdYPtd89KyiN93hlo9WhUruygDnn6QeZsWMG/TAlKu\nplDNt+BJvZreh6S4pBLPGz/nZWIjY1j/XcnBFLvX7aJjn063LL8yYk5KRFOtRv6+plp1TEkFLTvh\n5Iy2vh/uc+bjuXQFunua4T5jFtpGTctNgyEuGYdCv1GHWj4Yivyv3PDc2GQyTkVautdMZhI2HcK9\n5e2NB9Xw9iQuqaDVcTXpGjW9PWxsfv/rAH07twYgoEkDDHl5pKRnotNqmfzMw/z3k8l8PuU50jOz\nqV+rYh/8zIhSb5WRKuFgIL97K0RK+Q4wARh6q1MKnXtBSvkV0BMIEEL4FLW5wT5SyiVSykApZWBb\nt0YcXLaNrwZM46sB0zi79TCtH+kGQJ02jchJzyYjwbbJnJGQiiE9mzptLH23rR/pxtmtRwBsxmvu\n7RvI1bAoAK7FJOLXpRkAdk4O1GnTmMQLMTb5Hv5pG98MmMY3A6ZxbuthWg216Kh9XcfVIjqupmLI\nyKa2VUerod0I22bR4d2gZr5d0z7tSLoQC4BL9YJ/vFoBDREaQXZKRtEqIulYBG5+elzqVkdjp6X+\n4E5EbQ21sYneGkrDxywa6z3Ugfg9lqGzzOik/PEYrZMD1do2Ii08BgdvN+ysXYZaRzt8u7ckLdy2\nDkoi+KeNvNb/FV7r/woHtuynx9AHAGjSpimZ6VmkXC1+U3tq0gic3Zz59t1vbNJ9GxQ0aAN7tic2\n8tblV0aMYWfR1q6DpqYedDocuj9A3t9784/LrExShg8mdfQwUkcPw3j2DGkzp2EKP1duGtKOXsC5\noR7HetURdlr0Q7qQsOVwqc69djQcnYcLdj6WsSqvri3IsP6vlJXm/nW5HJtA1NUk8oxGNu87yv2B\nzW1sfKt58fep8wBERMWTm2fE292VbEMuWTmWB679J86h1WpsggMqAlmGrTJSJaLIhBBNAbOU8rw1\nqTVwCcug/XU0wKPACiwtlT3Wcx8EgqVlhLYxlii063ffwUKI2Vi614KAN8uiK2zHMRr3aM1/ds61\nhClP/jr/2LjgWXw1YBoAG97+noc/fRE7R3vOhxzPjyDrM/VJfJvVR0pJalQC66ZZIrcO/rSNIZ+8\nyIStH4EQHF25k/izNx5MDP/rGI16tGb8rrkYrWHK13kheBbfWHVsmv49gz57EZ2jPRdCjhO+w6Lj\ngTeH4dPQF2mWXItOJNiq494BHQgc0Quz0UReTh5rXl5UYvnSZObwWz/ywK9TEFoNF1bs5FpYNK0m\nDyXp+EWit4YSvnwnXRaMZdDezzCkZrB3nCWvsO+30WneGB7cMQchBBd+20XqP1fwvLcunT9/EaHR\nIDSCS+v/Jnr7sRLLvxFH/jpMYI9AFu/+BkO2gYWT5ucfm7dpAa/1fwUfvQ+PvzKMK+evMDfYEoIb\n/OMGtq3YyoPPPERA1wCMeSYyr2Uw//V5ZSq/NEx+Zw6Hjp4gNTWNnkNG8NJzIxk6sG/5FmI2kfnV\nfNzf/xQ0GgzbgjFdjsRpxGiM58+S9/e+m57uuXQFwtkFodNh17kr6dMnFY9AuwXSZObc1KW0XTEN\nodUQszyEzHNR+E95jLTjESRsOYJ7a38Cvp+InacL1fq0w3/yY+y/fxKYJWHvLqPdqrdBCNKPRxD9\n85+3VRU6rZapo4cy7sOv87u9GtX15YvfNtHcvy5BgS2Y+PRgZn79Gz9v3IkAZr70JEIIkq9lMO7D\nxWg0ghreHnw44anb0lAWKuvgfWkRFREZU94IIdoBCwFPwAiEY+kuWwVMklIeFkJkAEuAPsBV4Akp\nZYIQYgXQFsiynvuWlHKLEOJdoCEWp1MN+FhKafsIW4QZDZ6665Vld9cVWGiYd/eb5P/VVewAa2lR\nC44VUBkWHOu2ddTdlgCAY8CAO/4nWeVb+nvOo7G/3P1/yiJUiRaMlPII0KWEQ0GFbIoPDljSh90k\n6xNSyqfvTJ1CoVBUDKa7LeAOqRIORqFQKP4XqazRYaXlf9bBlNfb/AqFQlFRVNbosNLyP+tgFAqF\norJTSYZcbxvlYBQKhaKSorrIFAqFQlEhVPUwZeVgFAqFopJiUi0YhUKhUFQEqgWjUCgUigpBOZj/\nITwqwYib3ni3FVhI1t5tBeCpcbjbEoDK8Ra9+4/f320JALi3mnS3JSBjwu+2BAsBd56FvPu3nDui\nykx2qVAoFP9rlOeCY0KIfkKIc0KIcCFEsXkXhRAOQojfrMf/FkI0uFP9ysEoFApFJcVUhu1mCCG0\nwBdAf6AZ8KQQolkRs+eAFCllIyzraH10p/qVg1EoFIpKSjkuONYBCJdSRkgpc7HMOj+4iM1gLEvM\ng2Ui4Z5CiDvqpFMORqFQKCop5dhFVhsovOZHlDWtRBsppRG4Btx4jfFSoByMQqFQVFLK4mAKr75r\n3cbcJdn5qCgyhUKhqKSUZS4yKeUSLGtilUQ0ULfQfh1rWkk2UUIIHeABlLzGeClRLRiFQqGopJTj\nGMwhoLEQwk8IYQ8MA9YVsVkHXF+t7VHgL3mHK1KqFoxCoVBUUsprwTEppVEIMQHYAmiBpVLK00KI\nmcBhKeU64DtgmRAiHEjG4oTuCOVgboMe743Er0drjNkGNk9cwtVTkcVsarRsQL/PXkTnaM/FHcfY\n8c4yABw9XHjoywm416lOWlQC619aiOFaFt7+vvT9dAw1WjRg7ycrObwkGACvhr489MWE/Hy96tXg\n+CerOPftlvw036BWBL4/EqHREL48hDOL1tto0djr6LJgLN4t/TCkpLNn7CIyoxIROi2dPn0e75YN\nEDoNF1fu4fSi9TjX8qbz52Nxqu6BlJLwn3dw7rst3Ix6Qa3o/u5IhFbDmeUhHPmyuIY+88dSvaUf\nOSnpbH5pEelRiTh6utL/61eoEdCQsyt3sfPtn/LPaTy4M4ETBoGUZMansvWVL8lJySjdRbLy1Duj\nCejRltzsXL6ZtJBLpy8Wsxk6aTj3PXI/Lh4uvNh8RH768Lef4Z7OLQBwcHTArZoHL7Uq2wKodu06\n4DLmZdBoyNm6kZyVv5ZoZ9+lO25vvU/qq2MwhZ9DuLnjNm0musZNMWzfTObiz8tUblmYPmsuu/Ye\nxNvLk7U/L66wcjyC2lD//dEIjYary7cTu+h3m+P6MQOpMbwX0mgiLymNiNe/IDc6AefmDWgw+0W0\nbk5gMhO9YDXJ6/beto69/1zi4zV7MEszD3dqxuhe7WyOf/L7Hg6djwIgJ89Icno2e+a8AMC8dfvY\nfSYSaYZOTesw5ZFu3GGg1U0xl+OE/VLKYCC4SNqMQp9zgMfKrUCqiIMRQpiAk1j0/gOMklJm3WGe\nzwCBUsoJt7ItjF+PALwa6FnafSK+bfzp9eEz/Dr43WJ2vT58lm1vfEvs0Qs88uNkGgS1IjLkBB3G\nD+Ty3jMc/HI9HV4aSIeXBrJ79m9kp2by1zvLaNTX9seeEhHLsv5vWTRrBOMOLiRq0+GC76ERtJ81\nir+GzSErNpl+wTOJ2nKEtPMx+Tb+TwaRm5rJuvsmUn9wJ9pMH8aesYuoP7ADGgcdG3tORetkz0Mh\nHxG5dj+mXCOhM38l5WQkOhdH+m9+n9hdJ23ytKlLjSDog1GsHT6HjNhkntgwk4htR0gpZN98WBA5\nqZks6zaRxoM6cd+0YWx+aRFGQx4HPl2FT9M6+DStU5CnVkP3d0fwywNvkJOSQZdpw2j1TB8OzltT\n6mvVKqgtej9fpgRNwL9NY0Z9OIaZQ6YWszv25yG2/xjMxyGLbNJ/ff+H/M+9RvWnfnO/UpcNgEaD\ny7j/kDZ9IubEBDzmfU3egb2YrlyytXNywnHwo+SdPZ2fJHNzyVr2Hdr6fujql7HcMjJkQG+GDx3E\ntPc/rbhCNBoazHqBs8PeIzc2iebBH5O65RDZ1hs5QNapi5zqPxlzdi41nu5LvbefJnzsZ5izDVx4\ndQGGi7HY1fSixeZPuRZyFFNa2W8BJrOZ2at2sXjcIGp6uvLU3JXc38IPf713vs3kh7vmf16+6wRn\noxIAOHYxlmMXY1k5xfJg/+znazgcHkP7xkWDscqPqj5VTFUZg8mWUraWUrYAcoGxpT3R+oJRueHf\npx1nVu8BIPboBRzcXXCp4Wlj41LDEwdXJ2KPXgDgzOo9NOobaDm/dztOr9oNwOlVu2nUx5KenZRG\n/IkIzMYbN4rr3decjEtXyYwuGHfzaeNPemQ8GZcTMOeZuPTHAeoWcVJ1+rYlYqWlzMsbDlKza3MA\npASdswNCq0HraI8510heRjY5V1NJORkJgDEzh2vhMTj7enMjarb2JzUynjSrhrB1B2jYx1aDX5+2\nnLV+7/CNB6lzn0WDMdtA7KEwjIY8G3shBEII7Jwt08HYuzqRGZ9yQw0l0bZPe/au2QnAhaPncXZz\nwaO6ZzG7C0fPcy0h9aZ5dRrUlQPr9pSpfF2TezHFRGOOiwWjEcOuv7Dr1LWYnfOI58he9Svk5hYk\nGnIwnjkJebnF7MubwNYt8XB3q9AyXNs0IicyFsPleGSekeQ/9uDVt4ONTdq+U5izLd83IzQMe19L\nhGxORCyGi7EA5MWnkJd4DZ2Px23pOHXpKnWreVCnmgd2Oi192zQm5GTxVu11NoWep1+7JgAIBLl5\nJvKMZnKNJoxmMz5uTrelo7TIMmyVkariYAqzG2gEIIRYK4Q4IoQ4XTgkTwiRIYT4TAhxHOgshGgv\nhNgnhDguhDgohLj+31RLCLFZCHFeCPFxaQp31XuRHltwg0+PS8ZV71XcJi65RBvnau5kXrXczDKv\npuJczb3UX/yeQZ2JXLvfJs1J70VWTEFZWbHJOPna6nHWe5FptZEmM3lpWTh4u3J5w0GMWQYeObaI\nhw/N55/FweSmZtqc61KnGt4t6pMYeuGGulz0XmQU0pARe4M6KaQhNz0LRy/XG+ZpNprYMe17hm+b\nw+jDi/BuUpszK0JuaF8SXjW9SYpJzN9PjkvCS1/2sH6f2tWpXrcmZ/adKtN5Gp9qmBOv5u+bExPQ\n+lSzsdH6N0ZTvQZ5hw6UWVdVwl7vQ25Mwf9NbmwSdjd5aKn+ZE9S/wotlu7SuhEaex2GyLjb0nH1\nWgb6Qr+7mp6uXL2WWaJtTHIaMclpdLC2UAL89LRvXJteM76n94wf6HxPPRrqb/wdyoPynCrmblCl\nHIw1dK4/lu4ygNFSynZAIPCKEOL63cMF+FtKGQAcBH4DXrXu9wKyrXatgSeAlsATQojCYXyVCo2d\nFv/ebbm8/u9yy7Nam4ZIk5k1bV5mbcfXuXfsAFzrVc8/rnN2oNu3r3Jkxs8YM7JvklP5o9FpaTmy\nF8v7v8XSwAkk/XOZdhMG/asartNx4H0cCt6PNJfzv7EQuDw/nqxvvyzffKs4Po90x7VVI2K/WmuT\nblfDC/+FrxLx2iJL87uC2RIaTq8Af7Qay23yckIqEfEpbH1vFFvfG8WhsChCL5TcbVxeGIUs9VYZ\nqSoOxkkIcQw4DFzGEu0AFqdyHDiAJX67sTXdBKy2fm4KxEopDwFIKdOsb6kC/CmlvGYd3DoD1C9a\nsBBizNSpUy+dPXs2a+jGGWReTcXNt+Ap2E3vTUacbddNRlwKboWebArbZCWm5XepudTwJCsxrVQV\n4BcUQPypSHKK2GfHpeBcq6AsZ19vsmNt9WTFpeBitRFaDXbuzhiSM2jwcBdid5xAGk0YktJIOBSG\nd0BDi51OS7dvXyVyzT6uFBrzKYnMuBRcC2lw9b1BnRTSYO/mfNMB+2rNLZci7ZKlBXB+w9/4tmt8\nQ/vr9BzZj5nBnzIz+FNSr6bgU6ugxeCt9yElruxh/Z0G3lfm7jEAc1Iimmo18vc11apjSipoUQkn\nZ7T1/XCfMx/PpSvQ3dMM9xmz0DZqWuayKju5cUnY1yr4v7H39SEvNrmYnXu3VtR+9VHOPTMbmVsw\ndbjW1Ymmy94ias6vZISG3baOGh6uxBX63cWnZlDDw6VE281Hz9OvbcFv7q+TEbSqXxNnB3ucHey5\n7976HL/NllRphsgKYwAAIABJREFUUV1k/w7Xx2BaSylfllLmCiGCsLRGOltbJkcBR6t9jpSyNBF+\nhkKfTZQQ9CClXDJ79uz699xzj/PqB2cSvuUIzYZa+tF92/hjSM/K7/K6TubVVAwZ2fi28Qeg2dCu\nXNh6BIAL20Jp/mg3AJo/2o0L246UqgLuGdyZs3/sL5aedCwCNz89LnWro7HTUn9wJ6K22nYtRG8N\npeFjljLrPdSB+D1nLDqjk/LHY7RODlRr24i0cMsTWafPniftfAxnl2y6pbb44xF4NtDjbtXQZFAn\nLm6z1XBxWyj3WL93owc7ELX3zE3zzIxLxrtxbRy9Lb2Zdbu1JCX81k+Lfy7bzIwBk5gxYBKhWw9y\n3yP3A+DfpjHZ6Vm3HGspiq9/bZw9XAkPPVem8wCMYWfR1q6DpqYedDocuj9A3t8F0U8yK5OU4YNJ\nHT2M1NHDMJ49Q9rMaZjCy15WZSfjWDiOfr441K2BsNPhPbgrKVsP2dg4t/DD76OxnHtmNsaka/np\nwk5H4+/eIHFlCMkbi/8PlIXm9WpwOfEa0Ulp5BlNbDl6nvtbNChmdzE+hbQsAwEN9Plpvp5uHLkQ\ng9FkJs9k4siFaBrW9Cp2bnlS1bvIqkQU2Q3wwDLzZ5YQ4h6g0w3szgG+Qoj2UspD1vGX2+7vufjX\nMRr2COC53Z+Rl53LlkkFL86O3PRhfsTXn9N/oN9nY6xhyse5uOM4AAe/XM9DX71MiyfuJy06kQ3j\nFgLgXN2DERvex97VCWk20/a5fvzQ8w1yM7LROTlQv1sLtk1dStEeX2kyc/itH3ng1ykIrYYLK3Zy\nLSyaVpOHknT8ItFbQwlfvpMuC8YyaO9nGFIz2DvOEi0V9v02Os0bw4M75iCE4MJvu0j95wrVOzSh\n4WPdSDlzmf7bPgTg+Oz/EvPX8RLrRJrM7Hz7Rwb9PAWNVsOZ33aSHBZNx4lDuXriIhe3hXJmxU56\nzx/LyN0WDZvHF0Rsjdo3D3s3JzR2Ohr2DWTtU3NIOR/DwflrGLpqOmajifSoRLa/fqOXlEvm+I5Q\nWvVoyyc7v8CQbeDbyV/kH5sZ/CkzBljWLnn8zZF0HtwNeycH5u1fws7ftrN2/n8BS/fY3+tvMyTW\nbCLzq/m4v/8paDQYtgVjuhyJ04jRGM+fJe/vfTc93XPpCoSzC0Knw65zV9KnTyoegVYOTH5nDoeO\nniA1NY2eQ0bw0nMjGTqwb/kWYjIT+da3NP11BkKrIWHFn2SHXaH25GFkHr9A6tZD1Hv7abQujjRe\nYrkuudGJhD0zG++BXXDr1AydtxvVnugBQMR/FpJ1OrLMMnRaDW8O7ca4xeswmyWDO95LI18fvgz+\nm2b1ahDUwhKxtznU0nopHILcq7U/B89H8dhHKxACutxTj/tbVGyEX3mGKd8NxB2+qPmvIITIkFK6\nFklzANYCDbA4EU/gXSllSFF7IUR7YCHghMW59MLypmp+mLIQYgPwqZQy5EY6Pqs34q5XllpwrIDD\n2n93XOhGzG2eeGujCqayLDgWWgkWHGv1XdDdlgCAU/9X7vgFmSkNniz1PefjyOWVbnmyKtGCKepc\nrGkGLAP+t7S3jr8UbeH8YN2u2zx0pzoVCoWiPKmsXV+lpUo4GIVCofhfxFTFu8iUg1EoFIpKimrB\nKBQKhaJCkKoFo1AoFIqKQLVgFAqFQlEhVPUwZeVgFAqFopJStd2LcjAKhUJRaTFWcRejHIxCoVBU\nUtQg//8QlaGyWjlcu7XRv0Cb6OJTqf/b/OITdLclABB6WH9rowrGvRK8QQ/Q9kQFLlpWSirDbAIA\nHWNeueM81CC/QqFQKCoE1YJRKBQKRYWgWjAKhUKhqBBMVWAy4puhHIxCoVBUUtR7MAqFQqGoENQY\njEKhUCgqBDUGo1AoFIoKQXWRKRQKhaJCUF1k/8PUv78V9787EqHVcHpFCIe/XG9zXGuvo8+8sdRo\n6UdOSjrB4xeRHpWIo6crAxa/Qs2AhvyzchchM37KP2fwT1NwqeGBRqcl5uA5dkz/AWku/Y/MtXtb\nar3zAmg0pPy2jYTFq2yOV3tuMF5P9EGaTJiS0oh643PyohMAsKtVndpzXsbOtxpISeSz75EXffW2\n6mbe3Jn07/cAWdnZPPfcaxw9dqqYzZ/bVqL3rUl2dg4A/Qc8SUJCEk+PfJyP5kwnOiYOgC+//J6l\n3y8vVbn6Hq1oM9NyTSJ+DeHsIttrorHX0XHBOLxaNSA3JYN9Ly4kKyoRjZ2WwI+fwyugIZjNhL69\njIT9/6BzceSBtTPyz3eu5c2l1Xs4OuPnUunx6RFA0w+eQWg1RP/yF5EL/7A57tnpXpq+PwrXZvU4\n+eLnXN3wd/4xx9o+NJv7Ig61LNfj6FNzyLmSUKpyi+IR1Ib6749GaDRcXb6d2EW/2xzXjxlIjeG9\nkEYTeUlpRLz+BbnRCTg3b0CD2S+idXMCk5noBatJXrf3tjTciumz5rJr70G8vTxZ+/PiCikDqkZd\nXEdFkd1FhBAm4GShpCFSysh/pWyNIOiDUfz+1BwyYpMZtn4mEduOkHw+Jt+m+RNBGK5l8mP3iTQZ\n2ImuU4exafwijIY8Dny2Cp+mdfBpUscm300vLSQ3w7LW/IOLX6Hxgx0JW3+gdKI0GmrNHMvFkW9j\njEvC/4+5pG3/G0P4lXyT7NMRJA16HZljwPup/ujffJYrL38MQJ3PXiPhi/+SsecYGmfHMjm2wvTv\n9wCNG/lxT7OudOzQli8WzaZL14El2j799ASOhJ4olv7flet49T/Ty1Su0AjazXqGkCdmkx2bTO9N\n7xOzNZS0sOh8m4ZPBpF7LZPgLhOpO7gTAdOfZP/YhTR86gEAtjzwJg4+7nT/dQrb+r2NMTOHrb2n\n5Z/fe8sHRAUfLp0gjeCeOaMJffxDcmKS6LhlNglbDpNZSE9OdCKnX/2S+uOK10/zheO5OP93kned\nROvsgLzdm41GQ4NZL3B22HvkxibRPPhjUrccIvt8VL5J1qmLnOo/GXN2LjWe7ku9t58mfOxnmLMN\nXHh1AYaLsdjV9KLF5k+5FnIUU1rW7Wm5CUMG9Gb40EFMe78CZwOoInVxnareRaa52wLukGwpZetC\nW2RpThJC3LFjrdnan2uR8aRdTsCcZyJs/QEa9mlnY9OwT1vOrNoNwPngg9S9rzkAxmwDMYfCMObk\nFcv3unPR6LRo7HVlaiI7BzQm91IseVfikXlGrq3fhXvvjjY2mQdOInMMAGQdPYed3gcAh0Z1EVot\nGXuOAWDOysm3KysDB/Zl2S+WltPfB0Px8PRAr69xW3mVBe82/qRHxpNpvSaX/zhA7b6216RWv3ZE\n/ncXAFEbDlKzm+WauDepTfzeMwAYktLIu5aJd4CfzbmuDfU4+riTcOBsqfR4tG1E1sV4si9dReaZ\niFu7j+r92tvY5FxJIOPMZTDbDue6NKmN0GlJ3mV5fjJlGTBn55ayJmxxbdOInMhYDJctv4vkP/bg\n1beDjU3avlP5+WeEhmHva/ld5ETEYrgYC0BefAp5idfQ+Xjclo5bEdi6JR7ubhWS93WqSl1cx1yG\nrTJS1R1MMYQQDYQQu4UQodatizU9yJq+DjhjTRshhDgohDgmhPhaCKEtbTmuei/SY5Lz9zNik3Gt\n6WVj46L3IsNqI01mDOlZOHq53jLvIcum8MLRL8nLyCF848HSSkKn9yEvNjF/Py8uKd+BlIT3E71J\n33kEAAe/2pjSMqn31VQabZiPfuqzoLm9n0ftWnqirhS05KKjYqldq+T5ur79di6HD23lrWn/sUl/\n5OEBhB7Zxm8rllCnTq1Sleuk9yY7Oil/Pys2GSe97TVx1nuRVeia5KVlYe/tSuqZS9Tu0xah1eBS\ntzperfxwrm1bd/UGd+byulK2JgEHvTeGmAI9hpgkHIrouRHO/r4Y0zJptXQiHbfPofGMp0AjSl12\nYez1PuQW0pEbm4Sdr/cN7as/2ZPUv4rPNefSuhEaex2GyLjb0lEZqGp1IcvwVxmp6g7Gyeocjgkh\nrnekXgV6SynbAk8ACwrZtwVelVI2EULcaz1+n5SyNWACnvo3xd+ItSM/5tvACWjtdfmtnvLGc0gQ\nTi0bkbhkjSVBp8GlfTNiZy0lfPDr2NfV4/Vozwop+zojR71Mm7a9COrxMF3v68CIEY8CsGHjNvwb\nd6Jtu95s376L77+bX6E6AC4u30lWbDK9N39Am5kjSTx8HmmyfS6sN6Qzl9fuq3AtAEKrxbPjvZx/\nbxkH+07DqX5Nag0LqvByfR7pjmurRsR+tdYm3a6GF/4LXyXitUVQxccFSktlqAszstRbZaSqO5jC\nXWQPW9PsgG+EECeBlUCzQvYHpZQXrZ97Au2AQ0KIY9b9hkULEEKMEUIcFkIc3pdxPj89Iy4Ft1oF\nTz6uvt5kxKfYnJsZl4Kr1UZoNTi4OZOTklGqL2Yy5HFhWygNe7ctlT2AMS7JMkBvxU7vQ15cUjE7\nl/sCqD7+cSJf+ACZawQgLzaJ7H8uknclHkxm0rYdwKmFf6nLHjd2FIcPbeXwoa3ExsVTp25Bq6N2\nHd/8AfvCxFjTMjIyWb5iLe0DWwOQnJxCbq6li+K7pb/Stm3LUmnIjkvGqVCrw9nXm+w422uSFZeC\nc6FrYufuTG5yBtJk5tg7P7O19zT2PDsXe3dn0iMKNHs2q4dGqyHlRGSptAAY4pJxqFWgx6GWD4Yi\nem54bmwyGaciLd1rJjMJmw7h3tLv1ieWQG5cEvaFdNj7+pAXm1zMzr1bK2q/+ijnnpmd/7sA0Lo6\n0XTZW0TN+ZWM0LDb0lBZqGp1IaUs9VYZqeoOpiReA+KBACAQsC90LLPQZwH8WMhBNZVSvls0Mynl\nEilloJQysItr4/z0+OMRePrpca9bHY2dliYDOxGxzbYpHbEtlGaPdgOg8YAOXNl35qbC7ZwdcK7h\naRGn1eD3QGuSL8SW+otnnTiPQ4Na2NWpibDT4TGwO2nbbbvYHJs1pPaH47n0wvuYkgqm/s8+cR6t\nuwtab3cAXDq3Iuf85VKX/dXiHwls34fA9n1Yt24LI5+ytEY6dmhL2rU04uJso9G0Wi0+PpbuIp1O\nx4MP9uL06XMANuM1Awf24ezZ8FJpSD4WgZufHhfrNak3uBPRW47Y2MRsCaXB490BqPNQB+L3nLbo\ncbJH6+QAQM3uLTCbzDbBAfWGdObS2v2lrg+AtKMXcG6ox7FedYSdFv2QLiRsKV2AwLWj4eg8XLDz\nsYxJeHVtQUZY1C3OKpmMY+E4+vniULcGwk6H9+CupGw9ZGPj3MIPv4/Gcu6Z2RgL/S6EnY7G371B\n4soQkjeW7ftXRqpaXZiQpd4qI1U6iuwGeABRUkqzEGIUcKNxlT+BP4QQ86SUV4UQ3oCblPJSaQqR\nJjMhb//IkGVTEFoNZ37bSXJYNJ1eH0r8yYtc3BbK6d920nf+WEbt+oyc1Aw2TViUf/6ze+dh7+aE\nxk5Hw76BrB0xh5yUDAZ99zpaex1oBFH7/uHkz3+W/pubzMS8sxi/n96zhCmv3I7h/GVqvPYU2SfP\nk779IL5Tn0Xj4ki9L94EIC8mgUsvfABmM3GzluL3ywcIBNmnLpCyYmvpyy5E8KY/6dfvAc79s5es\n7Gyef/71/GOHD20lsH0fHBzsCd74K3Z2OrRaLX/+uZtvv/sFgJcnjOahh/pgNJpISU5l9PP/uVFR\nNkiTmdBpP3D/8jcsYcordpIWFk2LyUNJPn6RmK2hRCwPodPCcQzY9xm5qZnsH7sQAAcfd+5f/gZI\nSVZsCn+//JVN3nUHdWLXiI/LVA/SZObc1KW0XTENodUQszyEzHNR+E95jLTjESRsOYJ7a38Cvp+I\nnacL1fq0w3/yY+y/fxKYJWHvLqPdqrdBCNKPRxBdlt9CYUxmIt/6lqa/zkBoNSSs+JPssCvUnjyM\nzOMXSN16iHpvP43WxZHGSyxrqeRGJxL2zGy8B3bBrVMzdN5uVHuiBwAR/1lI1unI29NyEya/M4dD\nR0+QmppGzyEjeOm5kQwd2Ld8C6kidXGdf6vry3r/+w1oAEQCj0spU4rY1Ad+x9IwsQMWSilvGk8u\nKmvTqjQIITKklK5F0hoDq7EsZ70ZGC+ldBVCBAGTpJQPFbJ9ApiKpcLyrLY3HMX9vN6Iu15ZD2jV\ngmPXqSwLjnmbjbc2qmDctcUjEu8GasGxAjrGrLm9qIxC9KzTp9T3nD+jtt52eUKIj4FkKeUcIcSb\ngJeU8o0iNvZYfIZBCOEKnAK6SCljSsgSqOItmKLOxZp2HmhVKOkNa3oIEFLE9jcsXluhUCgqHf/i\n4P1gIMj6+Ucs90obByOlLBwn70Aphlj+P47BKBQKxf8L/sUw5ZpSyusDvnFAzZKMhBB1hRAngCvA\nRzdrvUAVb8EoFArF/2fKMlWMEGIMMKZQ0hIp5ZJCx7cDJb2Q9lbhHSmlFEKUWLCU8grQSghRC1gr\nhFglpYy/kSblYBQKhaKSUpYuMqszWXKT471udEwIES+E8JVSxgohfLG8T3izsmKEEKeAbsCqG9mp\nLjKFQqGopPyLL1quA0ZZP48C/ihqIISoI4Rwsn72AroC526WqXIwCoVCUUn5F1+0nAP0FkKcB3pZ\n9xFCBAohvrXa3Av8LYQ4DuwEPpVSniwxNyuqi0yhUCgqKf9WFJmUMgnLbCZF0w8Dz1s/b8M2QveW\nKAejUCgUlZTKOollaVEORqFQKCopJllZJ+IvHcrBlIG0kiP3/lVCjBW7/kRp+bZ6j7stgWXa0k0c\nWdGsWj/+bktAxpRuvraKpjK8RV8ZZhMoL6ryTCugHIxCoVBUWirrNPylRTkYhUKhqKSoMRiFQqFQ\nVAhm1UWmUCgUiopAtWAUCoVCUSGoKDKFQqFQVAiqi0yhUCgUFYLqIlMoFApFhaBaMAqFQqGoEFQL\nRmFD33efplGPAPKyc1k36WviTkUWs9G3aMDgz8aic7QjfMdxtrz7k83xTi8MoPf0p/i09Ytkp2Tc\nssx6Qa3o/u5IhFbDmeUhHPlyvc1xjb2OPvPHUr2lHzkp6Wx+aRHpUYk4errS/+tXqBHQkLMrd7Hz\nbYsOOxdHhq5+O/98V19vzq3Zy+73fr6hhtpBregwcyRCo+H88hBOflFcQ7fPx+LT0g9DSjo7xy0i\nIyoRAK9769L5o9HYuTqBWbLhwRmYDHlo7LR0/GAU+i73glkS+tFKLgUfumV9FGbMey8S2CMQQ7aB\n+RPnceHUBZvjDo4OvPnVVPT19ZjNZg5uP8iPc34AoOejvRj91miS4pIA2PDjerau2Fqm8vce+4eP\nvv8ds1nycM+OPDfEdkmO2MQUpn/xK+mZ2ZjNZl4d/hDd2jYjz2hk5pKVnLlwBY1GMOWZh2nfvFGZ\nyrbR8c8lPl6zB7M083CnZozu1c7m+Ce/7+HQ+SgAcvKMJKdns2fOCwDMW7eP3WcikWbo1LQOUx7p\nhhBlX/7dI6gN9d8fjdBouLp8O7GLfrc5rh8zkBrDeyGNJvKS0oh4/QtyoxNwbt6ABrNfROvmBCYz\n0QtWk7xu723WxM2ZPmsuu/YexNvLk7U/L66QMsqCSZrutoQ74o4djBDCBBSesnmIlDLyDvMcC2RJ\nKX8SQvwAbJBS3nBRGyHEaOA1QGJZguAtKeUfQoiZwC4p5fY70VNaGvUIwNtPzxf3T6R2m0YM+OBZ\nlg55p5jdgA9Hs+HNb4k+Gs6TP07BPyiACyHHAXD39aZht5akWm++t0JoBEEfjGLt8DlkxCbzxIaZ\nRGw7Qsr5gpVMmw8LIic1k2XdJtJ4UCfumzaMzS8twmjI48Cnq/BpWgefpnXy7fMyc1jRr2CRuyc2\nvs+FzTe+sQuNoOOHo9j65ByyYpN5KHgml7ce4VohDY2fDCL3WiZruk7Eb1An2r01jJ3jFiG0Grot\nGMfuVxeTcuYyDl6umPOMALR6ZTA5SWn83m0yCIGDp0up6uQ6gT0CqdWgFmO6v0DTNk156cPxTBz8\nejG7NUvWcHL/CXR2Oj5c/iHtgtpxJOQIALvX72LxjNu70ZjMZmZ9t5qvp4+lpo8nw6fOIyiwBf51\nChYV/Gb1Vvp2bs3jfe7jQlQcE2YvYVPbGazefgCA1Z9NIelaOuNnLeHX2a+h0ZR9hQ2T2czsVbtY\nPG4QNT1deWruSu5v4Ye/3jvfZvLDXfM/L991grNRCQAcuxjLsYuxrJwyDIBnP1/D4fAY2jeuXTYR\nGg0NZr3A2WHvkRubRPPgj0ndcohsq1MDyDp1kVP9J2POzqXG032p9/bThI/9DHO2gQuvLsBwMRa7\nml602Pwp10KOYkrLKnNd3IohA3ozfOggpr1fOaabqepTxZTHejDZUsrWhbbIO81QSrlYSvnTrS0t\ni+BgWfKzq5SyFdAJOGHNZ8a/5VwAmvRux4nVuwGIPhqOo7szrjU8bWxca3ji4OpE9FHL3FEnVu+m\naZ+Cp8k+M0by5+zlUMofVs3W/qRGxpN2OQFznomwdQdo2Mf26dSvT1vOrrLoCt94kDr3NQfAmG0g\n9lAYRkPeDfP39NPjVM2dmL9vvK5QtTb+pEfGk2HVcPGPA9Tra6uhXp+2hK+0aIjceBDfrhYNte5v\nSco/V0g5cxkAQ0oG0mz57o2H3c/JhdaWkJQYStGaK0zHPp34a/VfAJw7eg4Xdxe8anjZ2BhyDJzc\nfwIAY56RC6cuUM23WpnKuRGnwi9TV1+NOjWrYafT0a9LG0IOnbI1EoKMrBwAMrJyqO5lmWsuIiqO\nDi0sLRYfDzfcXJw4HXHl9nRcukrdah7UqeaBnU5L3zaNCTl58Yb2m0LP069dE4s8BLl5JvKMZnKN\nJoxmMz5uTmXW4NqmETmRsRguxyPzjCT/sQevvh1sbNL2ncKcnQtARmgY9r4+AORExGK4aFkuPi8+\nhbzEa+h8KmZOvsDWLfFwd6uQvG+Hf3HBsQqhQhYcE0I0EELsFkKEWrcu1vQgIcROIcQfQogIIcQc\nIcRTQoiDQoiTQgh/q927QohJRfJ8QAixttB+byHE70ANIB3IAJBSZkgpL1ptfhBCPGpdNOeYdTt5\nfb1pIYS/EGKzEOKIVe89d/K93fTepMUk5e+nxSXjVtP2huZW04u0uOQCm9hk3KxPkk16tyMtLpn4\nfy6XukwXvRcZMQX5ZcQm46q3LdNV70W61UaazOSmZ+Ho5Vqq/BsP6sT59QduauOs9yKzkIbM2GSc\ni2gobCNNZnLTsnDwcsWjoR6Q9P5lCgM3f0CLcQ8CYO/uDECbKY8ycPMHBH39Mo7V3Eul+To+eh8S\nYxPy95PiEvHR+9zQ3sXdhQ69OnJs7/H8tC4D7mPhlkVMXTy1zI7nanIqep+CB4waPh7EJ1+zsRn3\nWF827j5C77HvMn72Et4c/QgATRrUYufh0xhNJqKuJvFPxBXiE1PLVH6+jmsZ6Atd75qerly9llmi\nbUxyGjHJaXSwtlAC/PS0b1ybXjO+p/eMH+h8Tz0aFmr5lBZ7vQ+5hf43cmOTsPO9cT7Vn+xJ6l+h\nxdJdWjdCY6/DEBlXZg1VkX9xwbEKoTwcjFOhm/f1TtWrQG8pZVvgCWBBIfsAYCyW1dFGAk2klB2A\nb4GXb1LODuAeIUR16/6zwFLgOBAPXBRCfC+EGFj0RCnl4estLGAzcL39uwR4WUrZDpgEfFn0XCHE\nGCHEYSHE4cMZFTdjrc7Rnq7jB7Fz7g17Au8KTQZ1JuyP/RWWv9BqqdG+CbsmfEnwkJnU6x+Ib9fm\nCK0Gl1o+XD0cxvp+07l6JJz2M4ZXmA6NVsPkhVNY9/064i9bbl4Ht//N6C7P8nLfCRzdfZTX5hbv\nXrtTNu09yqCg9mxb/C5fTB3DWwt/wWw2M6RHR2p6ezD8zbl88sNaApr63Vb3WFnZEhpOrwB/tNay\nLiekEhGfwtb3RrH1vVEcCosi9ELMLXK5M3we6Y5rq0bEfrXWJt2uhhf+C18l4rVFpW7hV3XMUpZ6\nq4yUxyB/tvXGXRg7YJEQojVgApoUOnZIShkLIIS4AFwfNT0J3HAOeCmlFEIsA0YIIb4HOgNPSylN\nQoh+QHssK7LNE0K0k1K+WzQPIcQTQFugjxDCFegCrCw0YOlQQrlLsDgi3q//VLGrGPh0b9oMs8iO\nORGBe62CJ2R3vTfp8bZTyqfHp+Be6AnQ3deb9LhkvOvXxLNudcZsmp2f/sLGD/lu8AwyE2yfeguT\nGZeCa62C/Fx9vcmIsy0zIy4Ft1reZMYlI7Qa7N2cySlFd1O1e+shdBoSTkbe1C4rLgWXQhpcfL3J\nKqLhuk1WrFWDuzOGlAz+r70zD7OqPPLw++sGRAHZFEGHTUFAEZWgotHgOu7giI4YcHdcoxi3jLvB\nIVEnahTjRoiPyygEjYmJiBAR3Bd2xYggjbIIKNCyCUJT88d3bnNpuqEB73dud9f7PP1wzrn3Ur9e\n7qlbX9VXterrJSz8YHrp8tfcMVNo0rkNX789jbWrVvPliPEAzP7HB7Tv02OLmk8+92SOP/sEAGZM\n/ZxdWuxa+ljT5ruUJuzLctXdVzF/9nxeHrJhFPny4uWlx6OeH8UFN124RfvZNGvSiAWLN0QdixZ/\nx25NNl7aeWnM+zx686UA7L93G9asXcvS5Stp2rABN5z/H6XPO/fWB2m9+65sC80a1mdB1u97YfEK\nmjUsP581ctIMbjrjZ6XnYz6eRZfWu7HTDnUA+Gmn1kyZvYCue+2+VRp+WLCYOlnvjTotmrL26yWb\nPG/nI7qwR/8z+PT027Af1pVeL6y/Ix2euYW5dz/Hiomfb5XtqkxVryLL1UeiXxKiiv2BbkCdrMfW\nZB2vzzpfz5Yd3pNAP+BsYLiZrYPgfMzsQzP7LdAH6F32hZI6A3cCfcyshPC9F5fJH3Xaum8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oqQOC1OzhsD90XcaJk21wKXAPdlXcu+sUZZmspirqRGwF+B0ZKWEpLbsdhf0jJCJLNjcgwRq9nM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\nkTjQhtlWE+QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvl4HnL-YqTO",
        "colab_type": "text"
      },
      "source": [
        "we see that after adding familysize feature, we have a very strong correlation between sibsp& parch (expected)\n",
        "\n",
        "We can also split the family column into type of family. (small medium n large)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXcCgY4SY_e6",
        "colab_type": "code",
        "outputId": "361071ec-e057-42ce-baa4-eaef316cc310",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "train['Single'] = train['FamilySize'].map(lambda i: 1 if i==1 else 0)\n",
        "train['Small'] = train['FamilySize'].map(lambda i: 1 if i==2 else 0)\n",
        "train['Medium'] = train['FamilySize'].map(lambda i: 1 if 3<=i<=4 else 0)\n",
        "train['Large'] = train['FamilySize'].map(lambda i: 1 if i>4 else 0)\n",
        "train.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Single</th>\n",
              "      <th>Small</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Large</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  FamilySize  Single  Small  \\\n",
              "0      0         A/5 21171   7.2500   NaN        S           2       0      1   \n",
              "1      0          PC 17599  71.2833   C85        C           2       0      1   \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S           1       1      0   \n",
              "3      0            113803  53.1000  C123        S           2       0      1   \n",
              "4      0            373450   8.0500   NaN        S           1       1      0   \n",
              "\n",
              "   Medium  Large  \n",
              "0       0      0  \n",
              "1       0      0  \n",
              "2       0      0  \n",
              "3       0      0  \n",
              "4       0      0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1i3bgQcrZ9sG",
        "colab_type": "text"
      },
      "source": [
        "Similarly, lets map the sex column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJX7_wDMaL7-",
        "colab_type": "code",
        "outputId": "9d34dbf9-4a01-4948-c5d8-a8a0404efff7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "train['Sex'] = train['Sex'].map(lambda i : 1 if i=='male' else 0)\n",
        "train.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Single</th>\n",
              "      <th>Small</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Large</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name  Sex   Age  SibSp  Parch  \\\n",
              "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
              "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
              "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
              "\n",
              "             Ticket     Fare Cabin Embarked  FamilySize  Single  Small  \\\n",
              "0         A/5 21171   7.2500   NaN        S           2       0      1   \n",
              "1          PC 17599  71.2833   C85        C           2       0      1   \n",
              "2  STON/O2. 3101282   7.9250   NaN        S           1       1      0   \n",
              "3            113803  53.1000  C123        S           2       0      1   \n",
              "4            373450   8.0500   NaN        S           1       1      0   \n",
              "\n",
              "   Medium  Large  \n",
              "0       0      0  \n",
              "1       0      0  \n",
              "2       0      0  \n",
              "3       0      0  \n",
              "4       0      0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5jmLOwcaZrb",
        "colab_type": "code",
        "outputId": "f431a482-d1fb-4c02-95a6-cac095775e24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "train['Embarked_S'] = train['Embarked'].map(lambda i: 1 if i=='S' else 0)\n",
        "train['Embarked_C'] = train['Embarked'].map(lambda i: 1 if i=='C' else 0)\n",
        "train['Embarked_Q'] = train['Embarked'].map(lambda i: 1 if i=='Q' else 0)\n",
        "train.drop(['Embarked'],axis=1,inplace=True)\n",
        "train.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Single</th>\n",
              "      <th>Small</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Large</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name  Sex   Age  SibSp  Parch  \\\n",
              "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
              "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
              "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
              "\n",
              "             Ticket     Fare Cabin  FamilySize  Single  Small  Medium  Large  \\\n",
              "0         A/5 21171   7.2500   NaN           2       0      1       0      0   \n",
              "1          PC 17599  71.2833   C85           2       0      1       0      0   \n",
              "2  STON/O2. 3101282   7.9250   NaN           1       1      0       0      0   \n",
              "3            113803  53.1000  C123           2       0      1       0      0   \n",
              "4            373450   8.0500   NaN           1       1      0       0      0   \n",
              "\n",
              "   Embarked_S  Embarked_C  Embarked_Q  \n",
              "0           1           0           0  \n",
              "1           0           1           0  \n",
              "2           1           0           0  \n",
              "3           1           0           0  \n",
              "4           1           0           0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEeXHSubbI98",
        "colab_type": "text"
      },
      "source": [
        "Lets take a look at name feature\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dx1EXj4ByBl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titles = [i.split(',')[1].split('.')[0].strip() for i in train['Name']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKQnPc9dCXm-",
        "colab_type": "code",
        "outputId": "1b705c16-b33b-448f-e8f3-245ebe954d96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "train['Title'] = pd.Series(titles)\n",
        "train['Title'].head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Mr\n",
              "1     Mrs\n",
              "2    Miss\n",
              "3     Mrs\n",
              "4      Mr\n",
              "Name: Title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8T8mve53CgN9",
        "colab_type": "code",
        "outputId": "797ad490-0e6a-4d75-dd78-99a1d09b425a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "train['Title'].value_counts()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr              517\n",
              "Miss            182\n",
              "Mrs             125\n",
              "Master           40\n",
              "Dr                7\n",
              "Rev               6\n",
              "Col               2\n",
              "Major             2\n",
              "Mlle              2\n",
              "Don               1\n",
              "Ms                1\n",
              "Mme               1\n",
              "Lady              1\n",
              "Jonkheer          1\n",
              "Capt              1\n",
              "the Countess      1\n",
              "Sir               1\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0OmoJElClQB",
        "colab_type": "text"
      },
      "source": [
        "Many surnames are similar and some are very rare. So we can split them into 2 categories: Rare and frequent. In frequent we can split into very common surnames such as Mr. Ms. etc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANLEGcqjC0VW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rare_surnames = ['Rev','Col','Mlle','Don','Mme','Jonkheer','the Countess']\n",
        "mapping_other_surnames = {'Mr':1,\n",
        "                         'Mrs':2,\n",
        "                         'Miss':2,\n",
        "                         'Master':1,\n",
        "                         'Dr':3,\n",
        "                         'Col':1,\n",
        "                         'Major':3,\n",
        "                         'Ms':2,\n",
        "                         'Lady':2,\n",
        "                         'Capt':3,\n",
        "                         'Sir':1,\n",
        "                         'Rare':4}\n",
        "train['Title'] = train['Title'].replace(rare_surnames,'Rare')\n",
        "train['Title'] = train['Title'].map(mapping_other_surnames)\n",
        "train['Title']=train['Title'].astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-EziN_AEQv6",
        "colab_type": "code",
        "outputId": "be0ccd22-b903-4c51-953f-5b05be463bf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "train['Title'].head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    2\n",
              "3    2\n",
              "4    1\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyJE2hmSEU3c",
        "colab_type": "code",
        "outputId": "3bbce48e-3d4a-47cb-fc37-ff2bf260e3a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "train.drop(['Name'],axis=1,inplace=True)\n",
        "train.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Single</th>\n",
              "      <th>Small</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Large</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
              "0            1         0       3    1  22.0      1      0         A/5 21171   \n",
              "1            2         1       1    0  38.0      1      0          PC 17599   \n",
              "2            3         1       3    0  26.0      0      0  STON/O2. 3101282   \n",
              "3            4         1       1    0  35.0      1      0            113803   \n",
              "4            5         0       3    1  35.0      0      0            373450   \n",
              "\n",
              "      Fare Cabin  FamilySize  Single  Small  Medium  Large  Embarked_S  \\\n",
              "0   7.2500   NaN           2       0      1       0      0           1   \n",
              "1  71.2833   C85           2       0      1       0      0           0   \n",
              "2   7.9250   NaN           1       1      0       0      0           1   \n",
              "3  53.1000  C123           2       0      1       0      0           1   \n",
              "4   8.0500   NaN           1       1      0       0      0           1   \n",
              "\n",
              "   Embarked_C  Embarked_Q  Title  \n",
              "0           0           0      1  \n",
              "1           1           0      2  \n",
              "2           0           0      2  \n",
              "3           0           0      2  \n",
              "4           0           0      1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOKys5GhEYEX",
        "colab_type": "text"
      },
      "source": [
        "Lets drop passenger Id and take a look at Cabin feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8K0Ik-XFI8S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.drop(['PassengerId'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkGSfeXdFqFT",
        "colab_type": "code",
        "outputId": "dd44c9d6-84a9-47bc-f74d-8e96d885e095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Single</th>\n",
              "      <th>Small</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Large</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
              "0         0       3    1  22.0      1      0         A/5 21171   7.2500   NaN   \n",
              "1         1       1    0  38.0      1      0          PC 17599  71.2833   C85   \n",
              "2         1       3    0  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
              "3         1       1    0  35.0      1      0            113803  53.1000  C123   \n",
              "4         0       3    1  35.0      0      0            373450   8.0500   NaN   \n",
              "\n",
              "   FamilySize  Single  Small  Medium  Large  Embarked_S  Embarked_C  \\\n",
              "0           2       0      1       0      0           1           0   \n",
              "1           2       0      1       0      0           0           1   \n",
              "2           1       1      0       0      0           1           0   \n",
              "3           2       0      1       0      0           1           0   \n",
              "4           1       1      0       0      0           1           0   \n",
              "\n",
              "   Embarked_Q  Title  \n",
              "0           0      1  \n",
              "1           0      2  \n",
              "2           0      2  \n",
              "3           0      2  \n",
              "4           0      1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ul0_YelPFsr_",
        "colab_type": "code",
        "outputId": "62ec9190-a32f-4c27-dc61-d567f50463c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "train['Cabin'].describe()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count             204\n",
              "unique            147\n",
              "top       C23 C25 C27\n",
              "freq                4\n",
              "Name: Cabin, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSxEsRT8Fwnf",
        "colab_type": "code",
        "outputId": "56d716ad-3627-4565-a57e-70dc03d2de38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train['Cabin'][1][0]"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVa15FY6HH5u",
        "colab_type": "code",
        "outputId": "1a98cfd3-8f3d-4e99-ad87-a8b8d42a8c25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "train['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in train['Cabin']])\n",
        "train['Cabin'].head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    X\n",
              "1    C\n",
              "2    X\n",
              "3    C\n",
              "4    X\n",
              "Name: Cabin, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwquA10DHsp4",
        "colab_type": "code",
        "outputId": "8aa17010-0810-45e8-82a9-a2eec3e5e4ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(train['Cabin'])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130aef2a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AVNVZ+5h1cmdsAecNVYsk6cB5JbUkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoa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\nrv8PLzets9C07nUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjKY2kfxH0RB",
        "colab_type": "code",
        "outputId": "ebb5b3d9-a711-4054-ff4c-b57068e505c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.barplot(x='Cabin',y='Survived',data=train)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130af2d550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JmZMmuwL4ZlW9APh94HfbyiNJGq7NNYVzgV1VdW9VHQSuB9ZNmmYd8LH+808Br06SFjNJ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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiiC5NIxH_EJ",
        "colab_type": "code",
        "outputId": "9a3283aa-8999-4d94-dcbd-a4c554dd8cc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "train['Cabin'].value_counts()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "X    687\n",
              "C     59\n",
              "B     47\n",
              "D     33\n",
              "E     32\n",
              "A     15\n",
              "F     13\n",
              "G      4\n",
              "T      1\n",
              "Name: Cabin, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwLJKT-AIQiB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.get_dummies(train,columns=['Cabin'],prefix='Cabin')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iA95YSpNpcop",
        "colab_type": "code",
        "outputId": "4ae46cef-2cd2-4895-9bc8-b7f9a7c7cb7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Single</th>\n",
              "      <th>...</th>\n",
              "      <th>Title</th>\n",
              "      <th>Cabin_A</th>\n",
              "      <th>Cabin_B</th>\n",
              "      <th>Cabin_C</th>\n",
              "      <th>Cabin_D</th>\n",
              "      <th>Cabin_E</th>\n",
              "      <th>Cabin_F</th>\n",
              "      <th>Cabin_G</th>\n",
              "      <th>Cabin_T</th>\n",
              "      <th>Cabin_X</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
              "0         0       3    1  22.0      1      0         A/5 21171   7.2500   \n",
              "1         1       1    0  38.0      1      0          PC 17599  71.2833   \n",
              "2         1       3    0  26.0      0      0  STON/O2. 3101282   7.9250   \n",
              "3         1       1    0  35.0      1      0            113803  53.1000   \n",
              "4         0       3    1  35.0      0      0            373450   8.0500   \n",
              "\n",
              "   FamilySize  Single  ...  Title  Cabin_A  Cabin_B  Cabin_C  Cabin_D  \\\n",
              "0           2       0  ...      1        0        0        0        0   \n",
              "1           2       0  ...      2        0        0        1        0   \n",
              "2           1       1  ...      2        0        0        0        0   \n",
              "3           2       0  ...      2        0        0        1        0   \n",
              "4           1       1  ...      1        0        0        0        0   \n",
              "\n",
              "   Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_X  \n",
              "0        0        0        0        0        1  \n",
              "1        0        0        0        0        0  \n",
              "2        0        0        0        0        1  \n",
              "3        0        0        0        0        0  \n",
              "4        0        0        0        0        1  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zy5lo9STs0n7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.drop(['Ticket'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g50bqcpatkSM",
        "colab_type": "code",
        "outputId": "19970b89-afc5-467a-f2e0-cc0398556945",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Single</th>\n",
              "      <th>Small</th>\n",
              "      <th>...</th>\n",
              "      <th>Title</th>\n",
              "      <th>Cabin_A</th>\n",
              "      <th>Cabin_B</th>\n",
              "      <th>Cabin_C</th>\n",
              "      <th>Cabin_D</th>\n",
              "      <th>Cabin_E</th>\n",
              "      <th>Cabin_F</th>\n",
              "      <th>Cabin_G</th>\n",
              "      <th>Cabin_T</th>\n",
              "      <th>Cabin_X</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  FamilySize  Single  \\\n",
              "0         0       3    1  22.0      1      0   7.2500           2       0   \n",
              "1         1       1    0  38.0      1      0  71.2833           2       0   \n",
              "2         1       3    0  26.0      0      0   7.9250           1       1   \n",
              "3         1       1    0  35.0      1      0  53.1000           2       0   \n",
              "4         0       3    1  35.0      0      0   8.0500           1       1   \n",
              "\n",
              "   Small  ...  Title  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  \\\n",
              "0      1  ...      1        0        0        0        0        0        0   \n",
              "1      1  ...      2        0        0        1        0        0        0   \n",
              "2      0  ...      2        0        0        0        0        0        0   \n",
              "3      1  ...      2        0        0        1        0        0        0   \n",
              "4      0  ...      1        0        0        0        0        0        0   \n",
              "\n",
              "   Cabin_G  Cabin_T  Cabin_X  \n",
              "0        0        0        1  \n",
              "1        0        0        0  \n",
              "2        0        0        1  \n",
              "3        0        0        0  \n",
              "4        0        0        1  \n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPbGJGX6tl8n",
        "colab_type": "code",
        "outputId": "6d43f8c9-395c-4bb4-b073-d3bb8cddec14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train.shape"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aFEhzTrObtn",
        "colab_type": "text"
      },
      "source": [
        "Doing the same for test dataset. So instead of writing line by line, we can write a function which has all the lines same as training Eda and feature engineering."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fjmQl__Nvmk",
        "colab_type": "code",
        "outputId": "77834042-e971-46d5-e84c-9765baadeab0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass                                          Name     Sex  \\\n",
              "0          892       3                              Kelly, Mr. James    male   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
              "3          895       3                              Wirz, Mr. Albert    male   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
              "\n",
              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
              "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
              "1  47.0      1      0   363272   7.0000   NaN        S  \n",
              "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
              "3  27.0      0      0   315154   8.6625   NaN        S  \n",
              "4  22.0      1      1  3101298  12.2875   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3bhTZq7rYxB",
        "colab_type": "code",
        "outputId": "8c9ba25d-9d49-4bce-91ea-e395e83c70a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "test.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin          327\n",
              "Age             86\n",
              "Fare             1\n",
              "Embarked         0\n",
              "Ticket           0\n",
              "Parch            0\n",
              "SibSp            0\n",
              "Sex              0\n",
              "Name             0\n",
              "Pclass           0\n",
              "PassengerId      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6hlte_LuCMd",
        "colab_type": "code",
        "outputId": "ea014af2-d3e7-4730-8505-7a390e1f4b13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "test[test['Fare'].isna()==True]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>1044</td>\n",
              "      <td>3</td>\n",
              "      <td>Storey, Mr. Thomas</td>\n",
              "      <td>male</td>\n",
              "      <td>60.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass                Name   Sex   Age  SibSp  Parch Ticket  \\\n",
              "152         1044       3  Storey, Mr. Thomas  male  60.5      0      0   3701   \n",
              "\n",
              "     Fare Cabin Embarked  \n",
              "152   NaN   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMukajiLxFny",
        "colab_type": "code",
        "outputId": "8891413b-2e1a-4939-8937-44141ec92d8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test.shape"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XghMeK2irceM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def __cleaner__(df):\n",
        "test.drop(['PassengerId'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJqQHidyxDZf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titles = [i.split(',')[1].split('.')[0].strip() for i in test['Name']]\n",
        "test['Title'] = pd.Series(titles)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2pHf6XBxdO9",
        "colab_type": "code",
        "outputId": "d6418b22-bc67-4fa0-9bea-afdf5f06cad8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test['Title'].isna().sum()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvANuaAxxbCv",
        "colab_type": "code",
        "outputId": "0e4f4817-7c6f-414c-825f-fbfd1ab1f080",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "rare_surnames = ['Rev','Col','Mlle','Don','Mme','Jonkheer','the Countess']\n",
        "mapping_other_surnames = {'Mr':1,\n",
        "                         'Mrs':2,\n",
        "                         'Miss':2,\n",
        "                         'Master':1,\n",
        "                         'Dr':3,\n",
        "                         'Col':1,\n",
        "                         'Major':3,\n",
        "                         'Ms':2,\n",
        "                         'Lady':2,\n",
        "                         'Capt':3,\n",
        "                         'Sir':1,\n",
        "                         'Rare':4}\n",
        "test['Title'] = test['Title'].replace(rare_surnames,'Rare')\n",
        "test['Title'] = test['Title'].map(mapping_other_surnames)\n",
        "test.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass                                          Name     Sex   Age  SibSp  \\\n",
              "0       3                              Kelly, Mr. James    male  34.5      0   \n",
              "1       3              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
              "2       2                     Myles, Mr. Thomas Francis    male  62.0      0   \n",
              "3       3                              Wirz, Mr. Albert    male  27.0      0   \n",
              "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1   \n",
              "\n",
              "   Parch   Ticket     Fare Cabin Embarked  Title  \n",
              "0      0   330911   7.8292   NaN        Q    1.0  \n",
              "1      0   363272   7.0000   NaN        S    2.0  \n",
              "2      0   240276   9.6875   NaN        Q    1.0  \n",
              "3      0   315154   8.6625   NaN        S    1.0  \n",
              "4      1  3101298  12.2875   NaN        S    2.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0NgRXyJxqEd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test.drop(['Name'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekdek5ffxu3d",
        "colab_type": "code",
        "outputId": "bab5b393-10bf-4ee3-b3d4-01e5965dcc98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test.shape"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6Sb3GS1xDW6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test['Sex'] = test['Sex'].map(lambda i : 1 if i=='male' else 0)\n",
        "\n",
        "test['Embarked_S'] = test['Embarked'].map(lambda i: 1 if i=='S' else 0)\n",
        "test['Embarked_C'] = test['Embarked'].map(lambda i: 1 if i=='C' else 0)\n",
        "test['Embarked_Q'] = test['Embarked'].map(lambda i: 1 if i=='Q' else 0)\n",
        "test.drop(['Embarked'],axis=1,inplace=True)\n",
        "\n",
        "test.drop(['Ticket'],axis=1,inplace=True)\n",
        "\n",
        "test['FamilySize'] = test['Parch'] + test['SibSp'] +1\n",
        "test['Single'] = test['FamilySize'].map(lambda i: 1 if i==1 else 0)\n",
        "test['Small'] = test['FamilySize'].map(lambda i: 1 if i==2 else 0)\n",
        "test['Medium'] = test['FamilySize'].map(lambda i: 1 if 3<=i<=4 else 0)\n",
        "test['Large'] = test['FamilySize'].map(lambda i: 1 if i>4 else 0)\n",
        "\n",
        "test['Age'].fillna(test['Age'].median(),inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bT2le63Iygvq",
        "colab_type": "code",
        "outputId": "0752cdb6-3ee8-47ae-cba8-1666a22f9628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test['Fare'].fillna(test['Fare'].mean(),inplace=True)\n",
        "\n",
        "test['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in test['Cabin']])\n",
        "test = pd.get_dummies(test,columns=['Cabin'],prefix='Cabin')\n",
        "print(test.shape)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(418, 23)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3LSbCb3xDU8",
        "colab_type": "code",
        "outputId": "5e86fbbd-e871-4fd8-cdb7-00521ac46267",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Title</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>...</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Large</th>\n",
              "      <th>Cabin_A</th>\n",
              "      <th>Cabin_B</th>\n",
              "      <th>Cabin_C</th>\n",
              "      <th>Cabin_D</th>\n",
              "      <th>Cabin_E</th>\n",
              "      <th>Cabin_F</th>\n",
              "      <th>Cabin_G</th>\n",
              "      <th>Cabin_X</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare  Title  Embarked_S  Embarked_C  \\\n",
              "0       3    1  34.5      0      0   7.8292    1.0           0           0   \n",
              "1       3    0  47.0      1      0   7.0000    2.0           1           0   \n",
              "2       2    1  62.0      0      0   9.6875    1.0           0           0   \n",
              "3       3    1  27.0      0      0   8.6625    1.0           1           0   \n",
              "4       3    0  22.0      1      1  12.2875    2.0           1           0   \n",
              "\n",
              "   Embarked_Q  ...  Medium  Large  Cabin_A  Cabin_B  Cabin_C  Cabin_D  \\\n",
              "0           1  ...       0      0        0        0        0        0   \n",
              "1           0  ...       0      0        0        0        0        0   \n",
              "2           1  ...       0      0        0        0        0        0   \n",
              "3           0  ...       0      0        0        0        0        0   \n",
              "4           0  ...       1      0        0        0        0        0   \n",
              "\n",
              "   Cabin_E  Cabin_F  Cabin_G  Cabin_X  \n",
              "0        0        0        0        1  \n",
              "1        0        0        0        1  \n",
              "2        0        0        0        1  \n",
              "3        0        0        0        1  \n",
              "4        0        0        0        1  \n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-MmClxSxDSf",
        "colab_type": "code",
        "outputId": "90865182-0663-409f-e04e-1c518378ce81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "test.columns"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Title', 'Embarked_S',\n",
              "       'Embarked_C', 'Embarked_Q', 'FamilySize', 'Single', 'Small', 'Medium',\n",
              "       'Large', 'Cabin_A', 'Cabin_B', 'Cabin_C', 'Cabin_D', 'Cabin_E',\n",
              "       'Cabin_F', 'Cabin_G', 'Cabin_X'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxBwtTpixDP5",
        "colab_type": "code",
        "outputId": "32dc7a43-8b8f-4b46-e4ef-42fa2731c35d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
              "       'FamilySize', 'Single', 'Small', 'Medium', 'Large', 'Embarked_S',\n",
              "       'Embarked_C', 'Embarked_Q', 'Title', 'Cabin_A', 'Cabin_B', 'Cabin_C',\n",
              "       'Cabin_D', 'Cabin_E', 'Cabin_F', 'Cabin_G', 'Cabin_T', 'Cabin_X'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCdvwAjMxDNk",
        "colab_type": "code",
        "outputId": "5e9526ab-d099-42c2-b8a4-662da8a66980",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "test.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Title         1\n",
              "Cabin_X       0\n",
              "FamilySize    0\n",
              "Sex           0\n",
              "Age           0\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "Embarked_S    0\n",
              "Embarked_C    0\n",
              "Embarked_Q    0\n",
              "Single        0\n",
              "Cabin_G       0\n",
              "Small         0\n",
              "Medium        0\n",
              "Large         0\n",
              "Cabin_A       0\n",
              "Cabin_B       0\n",
              "Cabin_C       0\n",
              "Cabin_D       0\n",
              "Cabin_E       0\n",
              "Cabin_F       0\n",
              "Pclass        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiwHJt5wzwox",
        "colab_type": "code",
        "outputId": "a71e7bdc-6b70-47c2-cfbb-7469be5e05a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "train.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin_X       0\n",
              "Large         0\n",
              "Pclass        0\n",
              "Sex           0\n",
              "Age           0\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "FamilySize    0\n",
              "Single        0\n",
              "Small         0\n",
              "Medium        0\n",
              "Embarked_S    0\n",
              "Cabin_T       0\n",
              "Embarked_C    0\n",
              "Embarked_Q    0\n",
              "Title         0\n",
              "Cabin_A       0\n",
              "Cabin_B       0\n",
              "Cabin_C       0\n",
              "Cabin_D       0\n",
              "Cabin_E       0\n",
              "Cabin_F       0\n",
              "Cabin_G       0\n",
              "Survived      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqwOSPpIxDIU",
        "colab_type": "code",
        "outputId": "f2d614ee-a0ff-4623-cf72-a9e0859e9570",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "test[test['Title'].isna()==True]"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Title</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>...</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Large</th>\n",
              "      <th>Cabin_A</th>\n",
              "      <th>Cabin_B</th>\n",
              "      <th>Cabin_C</th>\n",
              "      <th>Cabin_D</th>\n",
              "      <th>Cabin_E</th>\n",
              "      <th>Cabin_F</th>\n",
              "      <th>Cabin_G</th>\n",
              "      <th>Cabin_X</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pclass  Sex   Age  SibSp  Parch   Fare  Title  Embarked_S  Embarked_C  \\\n",
              "414       1    0  39.0      0      0  108.9    NaN           0           1   \n",
              "\n",
              "     Embarked_Q  ...  Medium  Large  Cabin_A  Cabin_B  Cabin_C  Cabin_D  \\\n",
              "414           0  ...       0      0        0        0        1        0   \n",
              "\n",
              "     Cabin_E  Cabin_F  Cabin_G  Cabin_X  \n",
              "414        0        0        0        0  \n",
              "\n",
              "[1 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_9Sbl8ev_V2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test['Title'].fillna('1',inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GYwN5tkz2tY",
        "colab_type": "code",
        "outputId": "e7637684-ba35-4c75-94fb-d7dfb486fa4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "test.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin_X       0\n",
              "FamilySize    0\n",
              "Sex           0\n",
              "Age           0\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "Title         0\n",
              "Embarked_S    0\n",
              "Embarked_C    0\n",
              "Embarked_Q    0\n",
              "Single        0\n",
              "Cabin_G       0\n",
              "Small         0\n",
              "Medium        0\n",
              "Large         0\n",
              "Cabin_A       0\n",
              "Cabin_B       0\n",
              "Cabin_C       0\n",
              "Cabin_D       0\n",
              "Cabin_E       0\n",
              "Cabin_F       0\n",
              "Pclass        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7s0wJKx0FKd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test['Cabin_T']=0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjMQ4PNO6jZJ",
        "colab_type": "code",
        "outputId": "290118c0-f77b-45b0-9447-2b21cfcaea1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        }
      },
      "source": [
        "plt.figure(figsize=(20,12))\n",
        "sns.heatmap(train.corr(),annot=True)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130adfd4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DZpYp9+7Y5cX3NM+tmUy7fvdy8Kc/Ob0nju9fXY+5yMyj05+k75iH2bBsfZViEP7/u6Nz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\nMKolcT9Lg+4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QOh6opk/0fLb0m01eko6CjBxM+gJM+gJSDp+lWpNaZF8q6UHR4MWe1pgh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\nhz9sCXa0alf+5QoPAtfJIx+2BDt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eFmXVI5D0/SeR9XfDK6Jzjw5s/vUvAM6eOo+VtRUO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\n/AxMB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/m5X9JJV5ZvbtxP7sWbNJv9OLdjrpCy6xQ3NuwnYOVkeus0RE262zb1OfkxZvbW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\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fdGsxoDOtgvzYFhDC1qELuPBlzfKxy3AfrD017Ok/m9NzVuP94dRKw3l/OJXo4NXs6T8b\na08NLnLZbj/9YVIOnmPPgNmkHDxH++ljS69JPX6R/QEh7A8IKR0gAX1nNnHvafYOmsO+4Qu4eeVm\njbSW0G9YH1p5tGKS/zN8NH85wUtnVhn2vdeXMHXENKaOmHbfBkjaDvWmWVsNax8KJnzBGoYtfq7S\ncNfCI9nwcMVyDnB5yzHWjwpl/ajQBhsgAXhkdCArl3/QYPevgEpF+2XPc2byYk4MegOXRwdiZVTm\nXScPoygjm+P9pnN71VY8Fz0F6NuDzv+eweW5X3Fy8GyiH32b4kJdveS4ym3X9gHBRMxdg181bel2\no7YLwNLNkRZDupNzO6VOGhqqf5F+8TZbRi1i84hQwp78mP4f6gemakND1Yl1pbHqwRLqm18u/mcb\nu4YvZHdgCHFhUXSV+15eUwLJvHyHXQEh7Hv8A7zffhKVqbrSezdknYxK0OXNSSQfOFvuXjH/2cqp\n17+s1j4thvtg46lhd//ZRM5ZjU8V2nw+nEpk8Gp295+NjaeGFiV9wQNnCR8yjz3DFpB1LZ4OM/R9\nQV1+IRc+3MTZd9dXq+FeusL7zyb6HjbzkW0WLusqsVnmxVucmPoZqccu1in+cqhUtHp/GteefZeL\nAa/h8PBDmLdvXS5IYVwyscGfk/5H+UndoqR0rjw6l0ujZ3Fl3BxavPI4Ji6O9df0/zHFQjT6p6lS\nbW0vhNAJIaINPgtqenMhxBAhxNb6CBRC7BdC9KrjtdXGL4R4RAhxRghxUQhxTggxvqb3d+jpRc71\nBHJjk5AKdcT9fhRNUHmpmiA/bv+sH82O33ocZ/9u8vFexP1+lOKCIrSxyeRcT8Chp34wRJern5VT\nmapRmajB6C1GJjaWOPl3JWFHzWY0zLt1pOhWHEV3EqCoiJxd+7EaMqBcmLyI00jybGD+mb9Qt9A/\nLBXF3qEoVv86al1yKsVpGagcmtXURKW4B/lxddMhAJIjr2Jmb42lS/n7WLo0w8zWkuTIqwBc3XQI\n95EVk95jbF+u/XG03L2zY5PJuGT82uyqcRvpx82NBwFIi4zBzM4KCyM9Fi7NMLG1JC0yBoCbGw/i\nNtIPgKJsbWk4Eyvz0jTKT80k/fS1Wnc8jfWY1lJP4oGzSDr9ornUyBgs3fSVfovB3bn7Vyx3L8QC\nUJCeDcX39mXtHuRHTA3SytQgrWI2HaJNSVpJEma2lgCY2lqRm6ifzc1LzSSlDrYxxGOEH5d+0WtL\njLqKmZ01Vi4V82Ni1FVykyo+sMUd/YuivAJ9mMgYrDW1bxxbB/lxXbZPSjX2SZHtc33TIVpXkpcN\nse/QkoRD5wF9Piq4m4uTt8c9r7Hp6YX2RgL5sYlIhUWk/HEIx6De5cI4jOxD0s/7AUjdehT7Qd31\nJyRQWVmAWoXKwgypoAhdtha1rRV2/bqQ9MMefbDCInTyqqTqaBXkx41N+nycGhmDmX3l+djU1pJU\nOR/f2HSQVnI+rgqdNp+UE5fR1WPGu77p1uGZAM6v2EJxgX6QMT81s0bxugb5cetnvU3S5bJtbhSv\nuUszTGwsSZdtcuvng7jK8boG+RErXx9rcLwqTGwtcerXidgf9gMgFerIzsy55zXG+AcNZOem3QBc\niPwLG3sbnB5gR7LdCD/+kst5QtRVzO2ssa6knCdEXSWnknL+IOnl0x17O9sHFp+drxfa6wnk3UxC\nKiwi6ffDNDfKE81H9ibhZ/3DQfKWYzjI/Q6HId7kXLhJzgX9oFlRejYU12+xdcuRftzYWFbmq2q7\nypX5jeXLfM93n+bM+xsq9HVqSkP1L3R5BaXtqtrctE6vgWioOrGuNFY9WEJ980tVfS8kMJVXWptY\nWVCQkU1xUeV5uyHrZM/ng4jfdoL8lLvl7pdy6DxFOVqqw83g3un3so+BttifD+Ima0gy6Aumn4rB\n0tUJ0D9XpJ64VOc2VFOJrupsZmib7CtxZF+Nr1Pcxlj5tCf/RjwFt/T9nvQtB7EP7FsuTMHtJPIu\n3qjQ35UKi5DkvCvMTEGlrBVQqJqa5A6tJEk+Bp9lDa5KRghR+TDw/bu/N/AJME6SpE7AWOBDIUSN\nWicLVwe0cWVL9fPiU7FwdTAK41gaRtIVU5iVi5mjbSXXppVdqxI8FL6UEedWkfznWTKirpa7p2ZU\nL32Fm119dYSKrAAAIABJREFUhQugdmlOUUJy6feixBTULs2rDG/z6Ci0h05UOG7WrSOYmlJ0K65G\n8RpipXEgx+D35sSnYaVxqBgmPu2eYVr07Yg2+S5Z1xMBfSPZ7bUxRC//tVZ6LDWO5BroyY1Pw9Io\n7SxdHdDGlenRxqdhafBQ3W3BBP4R8QXujw3gnLwlqq4Y69HWQU8JbScOJkGebbJp5woS+G+Yz/Dd\nH9Dh1THVaqlpWuUapFWuQZjjb39Przcn8cTJz+m9aBKnlpbf2lUfrDUOZBtpszbSVlM6TxxM7P7a\nz8pZGtsnLg1LIw2WRvYxDtNxSiD/CF9Cv+UvYmZvBUD6+VhajfBFqFVYt3bGqUdbrNyc7qnFXONI\nwZ2y2deC+DTMNE4Vw8TJYXTF6DJzMXG0JXXrUYpz8+h9ejV+EauIW7mZooxszN1dKEzNxOufr9Nj\n98e0++QVVJbmNbSNYznb5MbVIO/Elc/H7aeMYFT4UvoufxFT2Tb3g/qmm207DS59OzJy6zsE/hKK\nk7dnjeK1qKzcVlK28+LLhylpD8yd7cmXBwLykzIwd7YvDefo154he5bS74d52HbUr2K0cnehIDWL\nnp9PY3DYEnw+fRELS4saaS3BWdOcpLiyNiM5PpnmmsrbjIXL5/LN7lU8O+upWsVxL2w0DmTFl6VV\ndkIaNrUs5+1H9+GpXUsYs3IGNq7/OzOF5hpH8g3ycX5cGubGZd7VkXy5XpB0xRRl5WLqaItVO1ck\nCXr8GIpf2Ie0fu3heuupaduVG1e+vSgp825BfmgT0siQB/LrQkP1LwCa92zHuL3LGLdnKUcXrC19\nAK0pTa1ObKx6sOze9csvAN0XTGBsxBe0Meh7XflmN7btW/Jw9AqC9i0jatF/qxx0a6g62ULjgOvo\n3lxfF15zg9RAW8XnCge0VWgzpM2kISTuja6zFkOM+595VfVR4+8d5n5gqnGiML6s31MYn4Kp5t59\npXLXuzan484v6HrsG5JW/kJRUlr1Fyn8LanzEJoQ4oYQYqm8uiRCCOErhNglhLgqhHjZIKidEGKb\nEOKSEGKlEEIlX/+lfN15IcS7Rvf9UAgRicHrfIQQKiHEOiHEB/L3EUKIo0KISCHERiGEjXx8pLwq\nJBJ4rJqfMQdYIknSdQD57xIguIrf/JKsOWJnbkytbVZjiiX+DFhIWM/XaNazHbadyi+lbfnoAOJ+\nO9IgUVuPHo55lw7c/XZjuePq5o44fzCf1Lc/qfNsz/3A45H+XDdYReIT/BgXvt5Jkbz65kFybtlG\ntvWaQeyvR/CaMuKBx18ZnWaOQ9LpiP3lMAAqtYrmfTpw4rV/s3/ce7Qc1QsX/64Nq+GZ4Zx4Zz0/\n957JiXfX4//piw0aX13o8OhAnHt4ElXLbVH3g8vfhvNH/9lsCwxFm5iB79tPAnD1xwPkxqcxauf7\n9HrvKZIjriDVc5b3Xtj09EIqLibC50Ui+7yC27SxmLu3QJiosenuScK3uzgzYi46bT4tpz/aYDoM\nifk2nK3932BHYEg52zQFVGoVZs1s2DnmHSLf38CgVa83io6S6vfumRvs7jWD/cMXcm3Nbvqs1Tdb\nKhMV9t3bcmNdOAcCQyjKzefJ1yc2iJb3pi/luYAXee3RWfTo052g8YENEk9tuRYexZoBs/g+KISb\nB88RtHxaY0tqEgi1Gvu+nfjr1S+IengRzUf3pdmgbo2mR21pRpcZD3Puo/pNMtwvjPsXAClRV/lj\n2AK2jn6L7q+P1a8oeYA0tTqxKdSDZ5dtZEuvGdw06HtphvQg4/xNNvu8zu6AEHyXPIuJjeUD0VNS\nJ3d7/xku1GNF1P2k48xxSEU6bsl9QYUyCuNTuDRyBhcemobD48MwaV771fH/S0hN4NNUqYnjVksh\nhOFQ5FJJkkqmhmMlSfIRQnwGrAMGAhbAOaDEg1kfoAtwE9iJfuBiExAqSVKavFpkjxCihyRJZ+Rr\nUiVJ8gWQB1xMgPXAOUmSFgshmgNvAgGSJOUIIeYDs4UQHwFfA8OAGKC6Keyu6FeSGBIBTK8ssCRJ\nXwFfAWzRTJLy4tOxNJjptXB1Ii8+vdw1efFpWLo5kRefhlCrMLW1oiAti4rXOla4tigzl5TDF3Ae\n6k3WRb1PADNHW5r5tKuxw1YAXVIKJhrn0u8mLZqjS6q499eib0/sX5hMwvPBUFi2JE9YW+Hyrw9I\nX7GW/LPVO5IsodOzAXSQ/SykRF/D2uD3Wrs6kptQ/vfmJqRjbTDjZxxGqFW0GdWbLaMWlR5z7ulF\n23/0oVfoRMzsrJCKJXT5hVxcF1ZBT7vnAvGU9aSdvoaVmxMl8xlWro5ojeyvjU8v3bYCYOnqiDah\n4ojzzV8PM+j7uVz45JcK5+5Fu+cC8ahCj2Ud9LR54iFcA3ry5xNLSo/lxqeRfOxiqSO8hL3RNOve\nliR5W0cJdUkrK4O0sjII4zVhUKkT1xtbjjPw4xeoD92eDaDLJL22pNPXsDHSlmOkrTpa+XfFb/rD\n/D5hcemS4ero8FxAqc+QVNk+JfPs1m6OaI00aI3sYxgmL6VseXLM+n0M/U7/cCvpijn1Ttl+4aDN\nb5FVzfLU/IQ0zFqWzfCbuTpSkJBaMYxbcwri00CtQm1nRVFaFs3nDCJjXzRSkY7C1EwyT17Exrsd\nmccukB+fSnaU3lF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ijt+CaW6SMiHL8i/ALwCSJC0E5kmS1EeW5XsbzCcQCAQCgUAgEAgE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UpOUAUJVbgqqgFFsPZ4vafCeGcvW7owDkJ13FxtURezNt9l7tsHG2Jz/pKgBXvzuKr5kNAfym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CN02GEa8DNGZ2YCRl5ZtcQl2hzcZg50H8XwILOKv0RkDOBlLmV7DsMN1WGISDz2O7lWdahs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\n",
            "text/plain": [
              "<Figure size 1440x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibtuONw0646M",
        "colab_type": "code",
        "outputId": "ed5c5451-a739-414c-d9db-17b78f884d18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        }
      },
      "source": [
        "plt.figure(figsize=(20,12))\n",
        "sns.heatmap(test.corr(),annot=True)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130a997630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FVPgG7PK329kRFs6OsHDi/jpG/SF6LS7Bfmiy1OQlFdeSl5RBYZYal2B9zqf6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XqsRpJIkmSUJOmUJElnJUn6VZIkhzL4ncMkSVpaFvGhkGiweDhHBr3H3g5T8e/XFqfaAVZJKg8K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+/L94hb5HlpGbeo+Tk/TXN+mXbhOz/SiBfy1FydNyZtbqguH/7b9+FeduLTBztKV/2OdcXPoz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\nmLmV5h0zV3vyE3TzV35CBmauj7Ypi6qwmMJCdbBaZnQMuXeTsPSUlyzs+iR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L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\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xbGr7NNqMPHy6tqb3ZzPJM8fn678f5dQ7m6usrSbtoETbUMIWj+WHtlPQZeTR9P5utJk2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\n90GuffybdT83yhz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v/xB9ehbhL35acnzwoU+wcrZHY2NF3QEdOPjEQgrSsujwn8lobK0R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\n",
            "text/plain": [
              "<Figure size 1440x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ggag20htDANQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "ecbd8a42-cc48-4322-b772-2b040dbf357d"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Title</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>...</th>\n",
              "      <th>Large</th>\n",
              "      <th>Cabin_A</th>\n",
              "      <th>Cabin_B</th>\n",
              "      <th>Cabin_C</th>\n",
              "      <th>Cabin_D</th>\n",
              "      <th>Cabin_E</th>\n",
              "      <th>Cabin_F</th>\n",
              "      <th>Cabin_G</th>\n",
              "      <th>Cabin_X</th>\n",
              "      <th>Cabin_T</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare Title  Embarked_S  Embarked_C  \\\n",
              "0       3    1  34.5      0      0   7.8292     1           0           0   \n",
              "1       3    0  47.0      1      0   7.0000     2           1           0   \n",
              "2       2    1  62.0      0      0   9.6875     1           0           0   \n",
              "3       3    1  27.0      0      0   8.6625     1           1           0   \n",
              "4       3    0  22.0      1      1  12.2875     2           1           0   \n",
              "\n",
              "   Embarked_Q  ...  Large  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  \\\n",
              "0           1  ...      0        0        0        0        0        0   \n",
              "1           0  ...      0        0        0        0        0        0   \n",
              "2           1  ...      0        0        0        0        0        0   \n",
              "3           0  ...      0        0        0        0        0        0   \n",
              "4           0  ...      0        0        0        0        0        0   \n",
              "\n",
              "   Cabin_F  Cabin_G  Cabin_X  Cabin_T  \n",
              "0        0        0        1        0  \n",
              "1        0        0        1        0  \n",
              "2        0        0        1        0  \n",
              "3        0        0        1        0  \n",
              "4        0        0        1        0  \n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnsGrema1wmm",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Model building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNa7w_M16bO5",
        "colab_type": "code",
        "outputId": "51d7281b-4f7f-4b21-dae0-7eec0510a927",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(train)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQI-XlnCG1ox",
        "colab_type": "code",
        "outputId": "31dee786-c159-4684-8aae-00e4c1e428d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X_train = train.drop(['Survived'],axis=1)\n",
        "y_train = train['Survived']\n",
        "print('Shape of X_train is : ',X_train.shape)\n",
        "print('Shape of Y_train is :',y_train.shape)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of X_train is :  (891, 24)\n",
            "Shape of Y_train is : (891,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTIrHjL_HrCg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kfold = StratifiedKFold(n_splits=10)\n",
        "classifiers = [\n",
        "    SVC(),\n",
        "    DecisionTreeClassifier(),\n",
        "    AdaBoostClassifier(DecisionTreeClassifier(),learning_rate=0.1),\n",
        "    RandomForestClassifier(n_estimators=50),\n",
        "    GradientBoostingClassifier(),\n",
        "    KNeighborsClassifier(),\n",
        "    LogisticRegression(),\n",
        "    LinearDiscriminantAnalysis(solver='eigen',shrinkage='auto'),\n",
        "    MLPClassifier(learning_rate='adaptive')\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCyDR5QAlDUt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "results = []\n",
        "for classifier in classifiers:\n",
        "  results.append(cross_val_score(estimator=classifier,X=X_train,y=y_train,cv=kfold,scoring='accuracy'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMBEoDW20d2e",
        "colab_type": "code",
        "outputId": "0c207d6d-cd29-4774-f29a-b3acd9dcf0e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "results"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.63333333, 0.66666667, 0.74157303, 0.76404494, 0.74157303,\n",
              "        0.73033708, 0.76404494, 0.76404494, 0.75280899, 0.81818182]),\n",
              " array([0.73333333, 0.78888889, 0.70786517, 0.7752809 , 0.7752809 ,\n",
              "        0.80898876, 0.82022472, 0.73033708, 0.86516854, 0.84090909]),\n",
              " array([0.68888889, 0.77777778, 0.69662921, 0.80898876, 0.82022472,\n",
              "        0.78651685, 0.82022472, 0.71910112, 0.87640449, 0.88636364]),\n",
              " array([0.74444444, 0.83333333, 0.76404494, 0.80898876, 0.84269663,\n",
              "        0.82022472, 0.79775281, 0.76404494, 0.83146067, 0.82954545]),\n",
              " array([0.8       , 0.78888889, 0.7752809 , 0.83146067, 0.8988764 ,\n",
              "        0.82022472, 0.82022472, 0.78651685, 0.86516854, 0.85227273]),\n",
              " array([0.64444444, 0.72222222, 0.69662921, 0.71910112, 0.73033708,\n",
              "        0.76404494, 0.74157303, 0.74157303, 0.71910112, 0.81818182]),\n",
              " array([0.8       , 0.83333333, 0.78651685, 0.86516854, 0.82022472,\n",
              "        0.7752809 , 0.82022472, 0.82022472, 0.84269663, 0.81818182]),\n",
              " array([0.64444444, 0.68888889, 0.70786517, 0.80898876, 0.65168539,\n",
              "        0.68539326, 0.71910112, 0.6741573 , 0.74157303, 0.71590909]),\n",
              " array([0.65555556, 0.71111111, 0.70786517, 0.83146067, 0.78651685,\n",
              "        0.75280899, 0.78651685, 0.82022472, 0.80898876, 0.81818182])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdb4JCZSmPpn",
        "colab_type": "code",
        "outputId": "3897d0d9-864d-40b7-9dde-a8d4d765d0ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "mean = []\n",
        "std = []\n",
        "for result in results:\n",
        "  mean.append(result.mean())\n",
        "  std.append(result.std())\n",
        "\n",
        "result_df = pd.DataFrame({'Cross Validation Mean':mean,'Cross Validation Error':std,'Algorithms':['Suppor vector classifier',\n",
        "                                                                                                  'Decision Tree classifier',\n",
        "                                                                                                  'AdaBoosting classifier',\n",
        "                                                                                                  'Random forest classifier',\n",
        "                                                                                                  'Gradient boosting',\n",
        "                                                                                                  'K Neighbours classifier',\n",
        "                                                                                                  'Logistic Regression classifier',\n",
        "                                                                                                  'Linear discriminant analysis',\n",
        "                                                                                                  'Multi layer perceptron classifier']})\n",
        "result_df"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cross Validation Mean</th>\n",
              "      <th>Cross Validation Error</th>\n",
              "      <th>Algorithms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.737661</td>\n",
              "      <td>0.049810</td>\n",
              "      <td>Suppor vector classifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.784628</td>\n",
              "      <td>0.048145</td>\n",
              "      <td>Decision Tree classifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.788112</td>\n",
              "      <td>0.065600</td>\n",
              "      <td>AdaBoosting classifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.803654</td>\n",
              "      <td>0.032909</td>\n",
              "      <td>Random forest classifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.823891</td>\n",
              "      <td>0.037111</td>\n",
              "      <td>Gradient boosting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.729721</td>\n",
              "      <td>0.042348</td>\n",
              "      <td>K Neighbours classifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.818185</td>\n",
              "      <td>0.024949</td>\n",
              "      <td>Logistic Regression classifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.703801</td>\n",
              "      <td>0.045352</td>\n",
              "      <td>Linear discriminant analysis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.767923</td>\n",
              "      <td>0.056005</td>\n",
              "      <td>Multi layer perceptron classifier</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cross Validation Mean  Cross Validation Error  \\\n",
              "0               0.737661                0.049810   \n",
              "1               0.784628                0.048145   \n",
              "2               0.788112                0.065600   \n",
              "3               0.803654                0.032909   \n",
              "4               0.823891                0.037111   \n",
              "5               0.729721                0.042348   \n",
              "6               0.818185                0.024949   \n",
              "7               0.703801                0.045352   \n",
              "8               0.767923                0.056005   \n",
              "\n",
              "                          Algorithms  \n",
              "0           Suppor vector classifier  \n",
              "1           Decision Tree classifier  \n",
              "2             AdaBoosting classifier  \n",
              "3           Random forest classifier  \n",
              "4                  Gradient boosting  \n",
              "5            K Neighbours classifier  \n",
              "6     Logistic Regression classifier  \n",
              "7       Linear discriminant analysis  \n",
              "8  Multi layer perceptron classifier  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAPJSNt6oSNI",
        "colab_type": "code",
        "outputId": "83f34085-7527-4591-ea7a-c69e6daaf949",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.barplot(x='Cross Validation Mean',y='Algorithms',data=result_df)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f130960e198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tkUVERMQCo5FFmXdSfYnTDKopjw2ovhNiCPAl2zd0d5ARXWGV5Zf1l/9rx94O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\nEKr2ByqB3/Fca8+rAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9jZVAb-oocx",
        "colab_type": "text"
      },
      "source": [
        "#### Hyper tuning parameters\n",
        "\n",
        "We will only tune the classifiers which helped us getting higher accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtnYdNUaqGEk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kfold = StratifiedKFold(n_splits=10)\n",
        "classifiers = [\n",
        "    AdaBoostClassifier(DecisionTreeClassifier(),learning_rate=1),\n",
        "    RandomForestClassifier(n_estimators=50),\n",
        "    GradientBoostingClassifier(),\n",
        "    LogisticRegression()\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okNWJNV8q8IT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ada_grid = {'base_estimator__criterion':['gini','entropy'],\n",
        "           'base_estimator__splitter':['best','random'],\n",
        "           'algorithm':['SAMME','SAMME.R'],\n",
        "           'n_estimators':[1,2,3],\n",
        "           'learning_rate':[0.01,0.1,0.5,1,1.3]}\n",
        "random_grid = {'max_depth':[None],\n",
        "              'max_features':[1,3,5,10,20],\n",
        "              'min_samples_split':[2,4,6,8],\n",
        "              'min_samples_leaf':[1,2,3,5],\n",
        "              'n_estimators':[50,100,200],\n",
        "              'criterion':['gini']}\n",
        "gradient_grid = {'loss':['deviance'],  #exponential\n",
        "                'learning_rate':[1,1.3],  #1,1.3\n",
        "                'n_estimators':[200,250],  # 100\n",
        "                'criterion':['mae','friedman_mse'],  #'friedman_mse'\n",
        "                'min_samples_split':[3], #0.5,4\n",
        "                'min_samples_leaf':[1,2], \n",
        "                'max_depth':[3],\n",
        "                'max_features':['sqrt']}\n",
        "logistic_grid = {'penalty':['l2'],\n",
        "                'C':[0.4,0.5,0.6,0.7],\n",
        "                'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
        "                'max_iter':[100,200,250,300]}\n",
        "\n",
        "main_grid = [ada_grid,random_grid,gradient_grid,logistic_grid]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGnjQ17hyaYA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid_results = list()\n",
        "grid_best_estimator = list()\n",
        "# for i in range(4):\n",
        "#   grid_ = GridSearchCV(estimator=classifiers[i],param_grid=main_grid[i],cv=kfold,scoring='accuracy',n_jobs=4)\n",
        "#   grid_.fit(X_train,y_train)\n",
        "#   grid_results.append(grid_.best_score_)\n",
        "#   grid_best_estimator.append(grid_.best_estimator_)\n",
        "  \n",
        "# grid_results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FmK1bHccOfv",
        "colab_type": "text"
      },
      "source": [
        "Dammmiddd ... taking too long to execute. ! ill pull one by one and execute !!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iktg_Ic0cZQf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid_ = GridSearchCV(estimator=classifiers[0],param_grid=main_grid[0],cv=kfold,scoring='accuracy')\n",
        "grid_.fit(X_train,y_train)\n",
        "grid_results.append(grid_.best_score_)\n",
        "grid_best_estimator.append(grid_.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eG6CfzZQc8zo",
        "colab_type": "code",
        "outputId": "34980ac0-65f5-4b44-a611-2ff88896d410",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grid_results"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8035914702581369]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsP-shq4dAHS",
        "colab_type": "code",
        "outputId": "0c53e38f-2971-4e86-9d73-d966b5b04f1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "grid_best_estimator"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[AdaBoostClassifier(algorithm='SAMME.R',\n",
              "           base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
              "             max_features=None, max_leaf_nodes=None,\n",
              "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "             min_samples_leaf=1, min_samples_split=2,\n",
              "             min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "             splitter='best'),\n",
              "           learning_rate=0.5, n_estimators=2, random_state=None)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrWPYiWYdGIV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid_ = GridSearchCV(estimator=classifiers[1],param_grid=main_grid[1],cv=kfold,scoring='accuracy')\n",
        "grid_.fit(X_train,y_train)\n",
        "grid_results.append(grid_.best_score_)\n",
        "grid_best_estimator.append(grid_.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxcfuxuWdPBS",
        "colab_type": "code",
        "outputId": "cf1ca0da-3ea0-4963-eedb-62cb6bdf712e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grid_results"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8035914702581369, 0.8383838383838383]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suLmFkI9dO94",
        "colab_type": "code",
        "outputId": "431a65fd-fbc8-4f5d-c9b5-93df22e1ef4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "grid_best_estimator"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[AdaBoostClassifier(algorithm='SAMME.R',\n",
              "           base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
              "             max_features=None, max_leaf_nodes=None,\n",
              "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "             min_samples_leaf=1, min_samples_split=2,\n",
              "             min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "             splitter='best'),\n",
              "           learning_rate=0.5, n_estimators=2, random_state=None),\n",
              " RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "             max_depth=None, max_features=10, max_leaf_nodes=None,\n",
              "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "             min_samples_leaf=2, min_samples_split=2,\n",
              "             min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=None,\n",
              "             oob_score=False, random_state=None, verbose=0,\n",
              "             warm_start=False)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtBIK1rNdOGC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid_ = GridSearchCV(estimator=classifiers[3],param_grid=main_grid[3],cv=kfold,scoring='accuracy')\n",
        "grid_.fit(X_train,y_train)\n",
        "grid_results.append(grid_.best_score_)\n",
        "grid_best_estimator.append(grid_.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IrkIsGWGdOV8",
        "colab_type": "code",
        "outputId": "86671690-c388-4059-b25e-bd886117aa49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grid_results"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8035914702581369, 0.8383838383838383, 0.819304152637486]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4i00ysl6dOST",
        "colab_type": "code",
        "outputId": "c0e7d44a-357d-4aba-ddbc-26bf224e0c7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "grid_best_estimator"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[AdaBoostClassifier(algorithm='SAMME.R',\n",
              "           base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
              "             max_features=None, max_leaf_nodes=None,\n",
              "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "             min_samples_leaf=1, min_samples_split=2,\n",
              "             min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "             splitter='best'),\n",
              "           learning_rate=0.5, n_estimators=2, random_state=None),\n",
              " RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "             max_depth=None, max_features=10, max_leaf_nodes=None,\n",
              "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "             min_samples_leaf=2, min_samples_split=2,\n",
              "             min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=None,\n",
              "             oob_score=False, random_state=None, verbose=0,\n",
              "             warm_start=False),\n",
              " LogisticRegression(C=0.6, class_weight=None, dual=False, fit_intercept=True,\n",
              "           intercept_scaling=1, max_iter=100, multi_class='warn',\n",
              "           n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
              "           tol=0.0001, verbose=0, warm_start=False)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hn1BIRG8dOuV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid_ = GridSearchCV(estimator=classifiers[2],param_grid=main_grid[2],cv=kfold,scoring='accuracy')\n",
        "grid_.fit(X_train,y_train)\n",
        "grid_results.append(grid_.best_score_)\n",
        "grid_best_estimator.append(grid_.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8teoX_lEdNwD",
        "colab_type": "code",
        "outputId": "93b31b6a-9267-4dde-d845-40a9507cfc1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grid_results"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8035914702581369, 0.8383838383838383, 0.819304152637486, 0.819304152637486]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ce0nDjUgdNse",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "75da5ffd-247d-4054-8f09-534dac945a7f"
      },
      "source": [
        "grid_best_estimator"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[AdaBoostClassifier(algorithm='SAMME.R',\n",
              "           base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
              "             max_features=None, max_leaf_nodes=None,\n",
              "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "             min_samples_leaf=1, min_samples_split=2,\n",
              "             min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "             splitter='best'),\n",
              "           learning_rate=0.5, n_estimators=2, random_state=None),\n",
              " RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "             max_depth=None, max_features=10, max_leaf_nodes=None,\n",
              "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "             min_samples_leaf=2, min_samples_split=2,\n",
              "             min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=None,\n",
              "             oob_score=False, random_state=None, verbose=0,\n",
              "             warm_start=False),\n",
              " LogisticRegression(C=0.6, class_weight=None, dual=False, fit_intercept=True,\n",
              "           intercept_scaling=1, max_iter=100, multi_class='warn',\n",
              "           n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
              "           tol=0.0001, verbose=0, warm_start=False),\n",
              " GradientBoostingClassifier(criterion='mae', init=None, learning_rate=1,\n",
              "               loss='deviance', max_depth=3, max_features='sqrt',\n",
              "               max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
              "               min_impurity_split=None, min_samples_leaf=1,\n",
              "               min_samples_split=3, min_weight_fraction_leaf=0.0,\n",
              "               n_estimators=200, n_iter_no_change=None, presort='auto',\n",
              "               random_state=None, subsample=1.0, tol=0.0001,\n",
              "               validation_fraction=0.1, verbose=0, warm_start=False)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNJpmxZrmkEl",
        "colab_type": "text"
      },
      "source": [
        "I have shuffled the second index with third because the second classifier(that is : graient boosting classifier) takes too long to execute."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33I0XHRFdrES",
        "colab_type": "text"
      },
      "source": [
        "Now lets do a voting clssification to rate each of the best results estimators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZvGE7ca0unf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "670ef10b-b666-4762-fc72-cf2457f07e45"
      },
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "voting = VotingClassifier(estimators=[('Random_forest',grid_best_estimator[1]),\n",
        "                                      ('Ada_boost',grid_best_estimator[0]),\n",
        "                                      ('Gradient_boost',grid_best_estimator[3]),\n",
        "                                      ('Logistic',grid_best_estimator[2])],\n",
        "                         voting='hard')\n",
        "\n",
        "voting.fit(X_train,y_train)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('Random_forest', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "            max_depth=None, max_features=10, max_leaf_nodes=None,\n",
              "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "            min_samples_leaf=2, min_samples_split=2,\n",
              "           ...enalty='l2', random_state=None, solver='lbfgs',\n",
              "          tol=0.0001, verbose=0, warm_start=False))],\n",
              "         flatten_transform=None, n_jobs=None, voting='hard', weights=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wk-wFvJfC0fK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "af03404d-f013-4837-9e27-93e9f991f8ba"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Title</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>...</th>\n",
              "      <th>Large</th>\n",
              "      <th>Cabin_A</th>\n",
              "      <th>Cabin_B</th>\n",
              "      <th>Cabin_C</th>\n",
              "      <th>Cabin_D</th>\n",
              "      <th>Cabin_E</th>\n",
              "      <th>Cabin_F</th>\n",
              "      <th>Cabin_G</th>\n",
              "      <th>Cabin_X</th>\n",
              "      <th>Cabin_T</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare Title  Embarked_S  Embarked_C  \\\n",
              "0       3    1  34.5      0      0   7.8292     1           0           0   \n",
              "1       3    0  47.0      1      0   7.0000     2           1           0   \n",
              "2       2    1  62.0      0      0   9.6875     1           0           0   \n",
              "3       3    1  27.0      0      0   8.6625     1           1           0   \n",
              "4       3    0  22.0      1      1  12.2875     2           1           0   \n",
              "\n",
              "   Embarked_Q  ...  Large  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  \\\n",
              "0           1  ...      0        0        0        0        0        0   \n",
              "1           0  ...      0        0        0        0        0        0   \n",
              "2           1  ...      0        0        0        0        0        0   \n",
              "3           0  ...      0        0        0        0        0        0   \n",
              "4           0  ...      0        0        0        0        0        0   \n",
              "\n",
              "   Cabin_F  Cabin_G  Cabin_X  Cabin_T  \n",
              "0        0        0        1        0  \n",
              "1        0        0        1        0  \n",
              "2        0        0        1        0  \n",
              "3        0        0        1        0  \n",
              "4        0        0        1        0  \n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzrHVU7yQxWH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = pd.Series(voting.predict(test),name='Survived')\n",
        "results = pd.concat([ids,predictions],axis=1)\n",
        "results.to_csv('Predictions.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOm-EuaL73hs",
        "colab_type": "text"
      },
      "source": [
        "## thanks !! \n",
        "\n",
        "**Credits : Praveen kumar !!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNKuzjDLLpgO",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title\n",
        "# LIST OF ALL THE ML ALGORITHMS I'LL TRY IN NEXT COMPETITION IF SUITABLE.\n",
        "\n",
        "# MLA = [\n",
        "#     #Ensemble Methods\n",
        "#     ensemble.AdaBoostClassifier(),\n",
        "#     ensemble.BaggingClassifier(),\n",
        "#     ensemble.ExtraTreesClassifier(),\n",
        "#     ensemble.GradientBoostingClassifier(),\n",
        "#     ensemble.RandomForestClassifier(),\n",
        "\n",
        "#     #Gaussian Processes\n",
        "#     gaussian_process.GaussianProcessClassifier(),\n",
        "    \n",
        "#     #GLM\n",
        "#     linear_model.LogisticRegressionCV(),\n",
        "#     linear_model.PassiveAggressiveClassifier(),\n",
        "#     linear_model.RidgeClassifierCV(),\n",
        "#     linear_model.SGDClassifier(),\n",
        "#     linear_model.Perceptron(),\n",
        "    \n",
        "#     #Navies Bayes\n",
        "#     naive_bayes.BernoulliNB(),\n",
        "#     naive_bayes.GaussianNB(),\n",
        "    \n",
        "#     #Nearest Neighbor\n",
        "#     neighbors.KNeighborsClassifier(),\n",
        "    \n",
        "#     #SVM\n",
        "#     svm.SVC(probability=True),\n",
        "#     svm.NuSVC(probability=True),\n",
        "#     svm.LinearSVC(),\n",
        "    \n",
        "#     #Trees    \n",
        "#     tree.DecisionTreeClassifier(),\n",
        "#     tree.ExtraTreeClassifier(),\n",
        "    \n",
        "#     #Discriminant Analysis\n",
        "#     discriminant_analysis.LinearDiscriminantAnalysis(),\n",
        "#     discriminant_analysis.QuadraticDiscriminantAnalysis(),\n",
        "\n",
        "    \n",
        "#     #xgboost: http://xgboost.readthedocs.io/en/latest/model.html\n",
        "#     XGBClassifier()    \n",
        "#     ]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zH_B7S2LGwD5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}